!pip uninstall spacy -y
!pip install spacy
!python -m spacy download en_core_web_sm


#!pip install spacy==2.1.1
#!pip install spacy textblob
!pip install matplotlib seaborn
#!pip install neuralcoref
!pip install --upgrade matplotlib networkx

Found existing installation: spacy 3.7.4
Uninstalling spacy-3.7.4:
  Successfully uninstalled spacy-3.7.4
Collecting spacy
  Using cached spacy-3.7.4-cp39-cp39-macosx_10_9_x86_64.whl (6.9 MB)
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Installing collected packages: spacy
Successfully installed spacy-3.7.4
Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.9/runpy.py", line 188, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/opt/anaconda3/lib/python3.9/runpy.py", line 147, in _get_module_details
    return _get_module_details(pkg_main_name, error)
  File "/opt/anaconda3/lib/python3.9/runpy.py", line 111, in _get_module_details
    __import__(pkg_name)
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/__init__.py", line 13, in <module>
    from . import pipeline  # noqa: F401
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/pipeline/__init__.py", line 1, in <module>
    from .attributeruler import AttributeRuler
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/pipeline/attributeruler.py", line 8, in <module>
    from ..language import Language
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/language.py", line 43, in <module>
    from .pipe_analysis import analyze_pipes, print_pipe_analysis, validate_attrs
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/pipe_analysis.py", line 6, in <module>
    from .tokens import Doc, Span, Token
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/tokens/__init__.py", line 1, in <module>
    from ._serialize import DocBin
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/tokens/_serialize.py", line 14, in <module>
    from ..vocab import Vocab
  File "spacy/vocab.pyx", line 1, in init spacy.vocab
  File "spacy/tokens/doc.pyx", line 49, in init spacy.tokens.doc
  File "/opt/anaconda3/lib/python3.9/site-packages/spacy/schemas.py", line 287, in <module>
    class TokenPattern(BaseModel):
  File "pydantic/main.py", line 197, in pydantic.main.ModelMetaclass.__new__
  File "pydantic/fields.py", line 506, in pydantic.fields.ModelField.infer
  File "pydantic/fields.py", line 436, in pydantic.fields.ModelField.__init__
  File "pydantic/fields.py", line 552, in pydantic.fields.ModelField.prepare
  File "pydantic/fields.py", line 661, in pydantic.fields.ModelField._type_analysis
  File "pydantic/fields.py", line 668, in pydantic.fields.ModelField._type_analysis
  File "/opt/anaconda3/lib/python3.9/typing.py", line 847, in __subclasscheck__
    return issubclass(cls, self.__origin__)
TypeError: issubclass() arg 1 must be a class
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import seaborn as sns
import matplotlib.pyplot as plt

from collections import Counter
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler

from textblob import TextBlob
import json
import pandas as pd
from nltk.corpus import state_union

import pandas as pd
#import neuralcoref
import networkx as nx
import matplotlib.pyplot as plt

with open('speeches.json', 'r') as file:
    json_data = json.load(file)

# Convert JSON data to a string
json_string = json.dumps(json_data, indent=4)  # indent is optional, for pretty-printing

# Load JSON data
with open('speeches.json', 'r') as file:
    data = json.load(file)

# Normalize JSON data
normalized_data = pd.json_normalize(data)  # This assumes 'data' is a list of records

# Create DataFrame
df = pd.DataFrame(normalized_data)

# Sort the DataFrame by the 'date' column
df_sorted = df.sort_values(by='date')

df_sorted.sample()

import nltk

# Download the required NLTK corpora
nltk.download('state_union')

# Now, you can safely load and use the 'state_union' corpus
from nltk.corpus import state_union

[nltk_data] Downloading package state_union to
[nltk_data]     /Users/brukeamare/nltk_data...
[nltk_data]   Package state_union is already up-to-date!

sOu = dict() #sOu=state Of the union
#Truman's 1945 and '46 speeches had different formats that the rest
truman45 = state_union.raw('1945-Truman.txt')
truman45 = truman45.strip('PRESIDENT')
title45 = 'STATE OF THE UNION'
name_t = truman45.split('\n', 3)[0][1:16]
name45 = name_t.title()
date45 = truman45.split('\n', 3)[2][0:16]
key45 = title45 + " " + date45
speech45 = truman45.split('\n', 3)[3]
sOu[key45] = [name45] + [date45] + [speech45]
truman46 = state_union.raw('1946-Truman.txt')
truman46 = truman46.strip('PRESIDENT')
title46 = 'STATE OF THE UNION'
name_t = truman46.split('\n', 3)[0][1:16]
name46 = name_t.title()
date46 = truman46.split('\n', 3)[2][0:16]
key46 = title46 + " " + date46
speech46 = truman46.split('\n', 3)[3]
sOu[key46] = [name46] + [date46] + [speech46]
#These Truman files have the same format, so grouped together
txt_files_T = ['1947-Truman.txt', '1948-Truman.txt', 
               '1949-Truman.txt', '1950-Truman.txt', '1951-Truman.txt']
for text in txt_files_T:
    x = state_union.raw(text)
    x = x.strip('PRESIDENT')
    k = x.split('\n', 3)[0][-18:]
    name1 = x.split('\n', 3)[0][1:16]
    name = name1.title()
    date = x.split('\n', 3)[2]
    key1 = k + " " + date 
    speech = x.split('\n', 3)[3]
    sOu[key1] = [name] + [date] + [speech]
    
#These Eisenhower files have two types of formats, so grouped together
txt_files_E = ['1953-Eisenhower.txt', '1954-Eisenhower.txt', '1955-Eisenhower.txt']
for text in txt_files_E:
    x = state_union.raw(text)
    x = x.strip('PRESIDENT')
    k = x.split('\n', 3)[0][-18:]
    name1 = x.split('\n', 3)[0][1:-59]
    name = name1.title()
    date = x.split('\n', 3)[2]
    key1 = k + " " + date 
    speech = x.split('\n', 3)[3]
    sOu[key1] = [name] + [date] + [speech]

no_date = ['1956-Eisenhower.txt', '1957-Eisenhower.txt', '1958-Eisenhower.txt',
            '1959-Eisenhower.txt', '1960-Eisenhower.txt']
for text in no_date:
    x = state_union.raw(text)
    x = x.strip('PRESIDENT')
    k = x.split('\n', 2)[0][-18:]
    name1 = x.split('\n', 2)[0][1:-59]
    name = name1.title()
    date = text[:4]
    key1 = k + " " + date 
    speech = x.split('\n', 2)[2]
    sOu[key1] = [name] + [date] + [speech]
sOu.keys()

dict_keys(['STATE OF THE UNION April 16, 1945', 'STATE OF THE UNION January 21, 1946', 'STATE OF THE UNION January 6, 1947', 'STATE OF THE UNION January 7, 1948', 'STATE OF THE UNION January 5, 1949', 'STATE OF THE UNION January 4, 1950', 'STATE OF THE UNION January 8, 1951', 'STATE OF THE UNION February 2, 1953', 'STATE OF THE UNION January 7, 1954', 'STATE OF THE UNION January 6, 1955', 'STATE OF THE UNION 1956', 'STATE OF THE UNION 1957', 'STATE OF THE UNION 1958', 'STATE OF THE UNION 1959', 'STATE OF THE UNION 1960'])

df_dict2 = pd.DataFrame(sOu)
df50s = df_dict2.T
df50s.columns = ['President', 'Date', 'Speech']
df50s.sample()

df50s.rename(columns={'Date': 'date'}, inplace=True)
df50s.rename(columns={'President': 'president'}, inplace=True)
df50s.rename(columns={'Speech': 'transcript'}, inplace=True)
# Rename the index
df50s.index.name = 'title'
df50s.reset_index(inplace=True)

df_sorted = df_sorted.reset_index(drop=True)

df_final = pd.concat([df_sorted, df50s], axis=0)

df_sorted_final = df_final.sort_values(by='date')

# Convert DataFrame to CSV
df_sorted_final.to_csv('output_filename.csv', index=False)

df_sorted_final['Year'] = df_sorted_final.date.str[:4]

df_sorted_final

df_sorted_final.to_csv('output_filename.csv', index=False)

df_sorted_final= pd.read_csv('output_filename.csv')
df_sorted_final

samp=df_sorted_final.loc[:, ['doc_name', "date", "transcript",'president','title', 'Year']]


# Assuming 'df' is your DataFrame
index_to_remove1 = samp.index[-2]  # Get the index of the second-to-last row


# Remove the second-to-last row cause this speech is alraady in the data
samp = samp.drop(index_to_remove1)
# Remove the second-to-last row
index_to_remove2 = samp.index[-3]
samp = samp.drop(index_to_remove2)
# Remove the second-to-last row
index_to_remove4 = samp.index[-7]
samp = samp.drop(index_to_remove4)
# Remove the second-to-last row
index_to_remove3 = samp.index[-7]
samp = samp.drop(index_to_remove3)

samp[-7:]
#samp.to_csv('samp_filename.csv', index=False)

samp.reset_index(inplace=True)

samp.drop('index', axis=1, inplace=True)

fukkk=samp[-12:]
fukkk

from datetime import datetime

for i in range(6):
    j=i+1
    um=samp.loc[(1054+j),'date']
    date_object = datetime.strptime(um, '%B %d, %Y')
    new_date_string = date_object.strftime('%Y-%m-%d')
    samp.loc[(1054+j),'date']=new_date_string
samp[-6:]

samp_sorted = samp.sort_values(by='date')
samp_sorted.reset_index(inplace=True)
samp_sorted['Year'] = samp_sorted.date.str[:4]
samp_sorted.drop('index', axis=1, inplace=True)
samp_sorted

samp_sorted.to_csv('outputfinal_filename.csv', index=False)

samp_sorted= pd.read_csv('outputfinal_filename.csv')
samp_sorted

zetemp=samp_sorted

!pip install selenium

from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from bs4 import BeautifulSoup
import requests
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time, os
import random
import pickle

from nltk.probability import FreqDist
from nltk.corpus import state_union
import re
import string
from nltk.stem import LancasterStemmer
nltk.download('state_union')

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[nltk_data] Downloading package state_union to
[nltk_data]     /Users/brukeamare/nltk_data...
[nltk_data]   Package state_union is already up-to-date!

True

zetemp.sort_values(by=['Year'], inplace=True)
zetemp.head(3)


# Text preprocessing steps - remove numbers, captial letters and punctuation

alphanumeric = lambda x: re.sub('\w*\d\w*', ' ', x)
punc_lower = lambda x: re.sub('[%s]' % re.escape(string.punctuation), ' ', x.lower())
no_n = lambda x: re.sub('\n', '', x)
no_r = lambda x: re.sub('\r', '', x)
no_hyphen = lambda x: re.sub('-', ' ', x)
zetemp['transcript'] = zetemp.transcript.map(alphanumeric).map(punc_lower).map(no_n).map(no_r).map(no_hyphen)
zetemp['Split'] = zetemp.transcript.str.split()
zetemp.head(3)


stemmer = LancasterStemmer()
zetemp['Stemmed'] = zetemp['Split'].apply(lambda x: [stemmer.stem(y) for y in x])# Stem every word.
zetemp.sample(3)



zetemp.to_csv('Presidents_Speeches.csv')


zetemp.to_pickle("my_Presidents.pkl")

with open('my_Presidents.pkl', 'wb') as picklefile:
    pickle.dump(zetemp, picklefile)
    
    
with open("my_Presidents.pkl", 'rb') as picklefile: 
    my_old_df = pickle.load(picklefile)

zetemp = my_old_df

zetemp.reset_index(level = 0, inplace = True)
zetemp = df.rename(columns = {'index':'Title'})
zetemp.head(3)

zetemp.president.value_counts()

Lyndon B. Johnson        71
Ronald Reagan            60
Barack Obama             51
Franklin D. Roosevelt    49
John F. Kennedy          45
Donald Trump             43
George W. Bush           40
Bill Clinton             39
Woodrow Wilson           33
Ulysses S. Grant         32
Andrew Johnson           31
Grover Cleveland         30
Herbert Hoover           30
Andrew Jackson           26
James K. Polk            25
Thomas Jefferson         24
George H. W. Bush        23
Richard M. Nixon         23
Benjamin Harrison        23
Jimmy Carter             22
James Madison            22
Theodore Roosevelt       22
Joe Biden                21
George Washington        21
Harry S. Truman          19
Warren G. Harding        18
John Tyler               18
Rutherford B. Hayes      16
Abraham Lincoln          15
Dwight D. Eisenhower     15
Franklin Pierce          15
William McKinley         14
Gerald Ford              14
James Buchanan           14
Calvin Coolidge          12
William Taft             12
Chester A. Arthur        11
Martin Van Buren         10
James Monroe             10
John Adams                9
John Quincy Adams         9
Millard Fillmore          7
Zachary Taylor            4
James A. Garfield         1
William Harrison          1
Name: president, dtype: int64

import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

plt.figure(figsize=(8, 14))
my_colors = ['orange','orange','orange','orange','orange','orange', 'darkviolet', 'darkviolet', 'orange', 'orange','darkviolet', 'orange',
            'orange', 'orange', 'darkviolet','orange', 'orange','orange', 'orange', 'orange', 'darkviolet', 'darkviolet', 'orange','darkviolet',
             'orange','darkviolet', 'orange', 'orange','darkviolet', 'darkviolet','orange','darkviolet', 'orange', 'darkviolet',
             'orange', 'darkviolet','darkviolet','darkviolet','darkviolet','orange', 'orange', 'darkviolet','darkviolet','darkviolet','darkviolet']

df.president.value_counts(ascending=True).plot(kind = 'barh', color=my_colors)
plt.xlabel('Number of Speeches', fontsize = 15)
plt.ylabel('Presidents', fontsize = 16)
plt.title('Number of Speeches per President    ', fontsize = 20)
plt.tight_layout()
plt.savefig('No._Pres_Speeches.pdf');

samp_sorted= pd.read_csv('outputfinal_filename.csv')
samp_sorted

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df= pd.read_csv('outputfinal_filename.csv')
df

# Count the number of words in each transcript
df['word_count'] = df['transcript'].str.split().str.len()

# Group by decade (assuming 'Year' column is the last 4 digits of the 'date' column)
df['decade'] = df['date'].str[:4].astype(int) // 10 * 10

# Increase font size and set a single color
sns.set(style='whitegrid')
plt.figure(figsize=(20, 10))
sns.boxplot(x='decade', y='word_count', data=df, color='lightblue')  # Adjust the color as needed

# Customize plot appearance
plt.title('Word Count of Presidential Speeches Over Decades', fontsize=24)
plt.xlabel('Decade', fontsize=18)
plt.ylabel('Word Count', fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xticks(rotation=45)  # Rotate x labels if needed

# Show the plot
plt.show()

litz=samp_sorted.loc[:, ["Year", "transcript"]]
fr=litz["Year"].drop_duplicates()
dates=[]

for i in fr:
    dates.append(i)

samp_sorted= pd.read_csv('outputfinal_filename.csv')

startyear=1939
endyear=1945


startDate=19390901 # Invasion of Poland, attack on Poland by Nazi Germany that marked the start of World War II. The invasion lasted from September 1 to October 5, 1939. As dawn broke on September 1, 1939, German forces launched a surprise attack on Poland.
endDate=19450814 # US President Harry S. Truman announced Japan's surrender and the end of World War II





gents = [
    "Europe", "Germany", "Hitler", "Jews", 
    "World War II",  'Navy', 'Army', 'Poland', 'Japan', 'Japanese'
]










bummy = samp_sorted.loc[(samp_sorted['Year'] >= startyear) & (samp_sorted['Year'] <= endyear)]
bummy.reset_index(inplace=True)
bummy.drop('index', axis=1, inplace=True)



df1 = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])


for i in range(len(bummy)):
    date_string = bummy.loc[bummy.index[i], 'date']  # Accessing the 'date' column for each row
    cleaned_string = date_string.replace('-', '')
    # Convert string to number
    result = int(cleaned_string)
    
    if startDate <= result <= endDate:
        
        # Assuming df1 is the DataFrame you want to add a row to, and df2 is the DataFrame from which you want to add the row
        row_to_add = bummy.loc[i]  # Select the row you want to add from df2

        df1.loc[len(df1)] = row_to_add.values
        
        
        
        
# Ensure the 'date' column is in datetime format
df1['date'] = pd.to_datetime(df1['date'])


# Create a DataFrame with each president's first and last speech date
president_dates = df1.groupby('president')['date'].agg(['min', 'max']).reset_index()
president_dates.columns = ['president', 'first_speech', 'last_speech']

# Print the new DataFrame
print(president_dates)





# Create an empty DataFrame to store the counts
fumpy = pd.DataFrame(columns=['president'] + gents)

# Iterate over each president in df1
for president in df1['president'].unique():
    # Initialize a dictionary to store the counts for each president
    president_counts = {'president': president}
    # Filter df1 for speeches by the current president
    president_df = df1[df1['president'] == president]
    # Iterate over each word in gents
    for word in gents:
        # Count the occurrences of the word in the current president's speeches
        word_count = sum(president_df['transcript'].str.count(word))
        # Add the count to the dictionary
        president_counts[word] = word_count
    # Append the counts for the current president to fumpy
    fumpy = fumpy.append(president_counts, ignore_index=True)
    
    
# Set the 'president' column as the index
fumpy.set_index('president', inplace=True)

# Calculate the sum of each column and sort the columns by the sum in descending order
fumpy = fumpy.loc[:, fumpy.sum().sort_values(ascending=False).index]


# Print the DataFrame fumpy
print(fumpy)

               president first_speech last_speech
0  Franklin D. Roosevelt   1939-09-03  1945-03-01
1        Harry S. Truman   1945-04-16  1945-08-09
                      Japan Germany Europe Japanese Hitler Poland Army Navy  \
president                                                                     
Franklin D. Roosevelt   149     107     92       86     55     38   51   49   
Harry S. Truman          19      23     15       10      2     17    3    0   

                      Jews World War II  
president                                
Franklin D. Roosevelt    0            0  
Harry S. Truman          0            0

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2261974942.py:30: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  bummy.drop('index', axis=1, inplace=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2261974942.py:84: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2261974942.py:84: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)

from sklearn.preprocessing import StandardScaler, MinMaxScaler
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle
from matplotlib.patches import Patch

# Fill missing values with zeros
fumpy_filled = fumpy.fillna(0)

# Convert fumpy to numeric type
fumpy_numeric = fumpy_filled.apply(pd.to_numeric)





# Set the size of the heatmap
plt.figure(figsize=(15, 7))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_transposed, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label == 'Franklin D. Roosevelt' else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 10.5  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx + 0.5, y_pos_below, label, ha='center', va='center', fontsize=20, color=color)
    start_date, end_date = date_mapping[label]
    plt.text(idx + 0.5, y_pos_below + 0.8, f'{start_date} - {end_date}', ha='center', va='center', fontsize=15, color='black')
    

legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility

plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=65)
plt.ylabel('Entities', fontsize=20)
plt.title(f'World War II Entity Frequency by Presidents \n {startyear} - {endyear} \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.2, "The heatmap displays the frequency of specific entities mentioned in President Speeches during the signifiacnt years of World War II, with annotations indicating the start and end dates of of presidents in office during that time period, which started with Invasion of Poland by Nazi Germany that marked the start of World War II and ended with the US President Harry S. Truman announcement of Japan's surrender. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)

plt.show()






# Initialize a StandardScaler
scaler = StandardScaler()

# Scale the data
fumpy_standardized = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)

# Set the size of the heatmap
plt.figure(figsize=(15, 7))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_standardized, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label == 'Franklin D. Roosevelt' else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 10.5  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx + 0.5, y_pos_below, label, ha='center', va='center', fontsize=20, color=color)
    start_date, end_date = date_mapping[label]
    plt.text(idx + 0.5, y_pos_below + 0.8, f'{start_date} - {end_date}', ha='center', va='center', fontsize=15, color='black')

    
legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility   
    
    
    
plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=65)
plt.ylabel('Entities', fontsize=20)
plt.title(f'World War II Entity Frequency by Presidents \n {startyear} - {endyear} Standardized \n', fontsize=25)


# Add a caption
plt.figtext(0.4, -0.2, "The heatmap displays the Standardized frequency of specific entities mentioned in President Speeches during the signifiacnt years of World War II, with annotations indicating the start and end dates of of presidents in office during that time period, which started with Invasion of Poland by Nazi Germany that marked the start of World War II and ended with the US President Harry S. Truman announcement of Japan's surrender. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)

plt.show()








# Initialize a MinMaxScaler
scaler = MinMaxScaler()

# Scale the data
fumpy_scaled = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)

# Set the size of the heatmap
plt.figure(figsize=(15, 7))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_scaled, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label == 'Franklin D. Roosevelt' else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 10.5  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx + 0.5, y_pos_below, label, ha='center', va='center', fontsize=20, color=color)
    start_date, end_date = date_mapping[label]
    plt.text(idx + 0.5, y_pos_below + 0.8, f'{start_date} - {end_date}', ha='center', va='center', fontsize=15, color='black')


legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility
    
    
    
plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=65)
plt.ylabel('Entities', fontsize=20)
plt.title(f'World War II Entity Frequency by Presidents \n {startyear} - {endyear} MinMax Scaled \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.2, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in President Speeches during the signifiacnt years of World War II, with annotations indicating the start and end dates of of presidents in office during that time period, which started with Invasion of Poland by Nazi Germany that marked the start of World War II and ended with the US President Harry S. Truman announcement of Japan's surrender. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)

plt.show()

samp_sorted= pd.read_csv('outputfinal_filename.csv')

startyear=1947
endyear=1991


startDate=19470312 # The Cold War began with the announcement of the Truman Doctrine in 1947, started a gradual winding down with the Sino-Soviet split between the Soviets and the People's Republic of China in 1961, and ended with the collapse of the Soviet Union in 1991.
endDate=19911225 # Gorbachev resigned on 25 December 1991 and what was left of the Soviet parliament voted to end itself. the Soviet Union itself dissolved into its component republics. Sign of the end of the cold war.


gents = [
    "the Soviet Union", "Russia", "Soviet",
    "Communist", "Soviets", "Communists", 'Army', 'Navy'
]










bummy = samp_sorted.loc[(samp_sorted['Year'] >= startyear) & (samp_sorted['Year'] <= endyear)]
bummy.reset_index(inplace=True)
bummy.drop('index', axis=1, inplace=True)




df1 = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])

prev=startDate

for i in range(len(bummy)):
    date_string = bummy.loc[bummy.index[i], 'date']  # Accessing the 'date' column for each row
    cleaned_string = date_string.replace('-', '')
    
    # Convert string to number
    if 'T' in cleaned_string:
        cleaned_string=cleaned_string.split('T')[0]
    result = int(cleaned_string)
    if result<9999:
        
        original_str = str(prev)
        bluuuh = str(result)
    
        # Cut off the last four characters
        last_four = original_str[-4:]

        # Combine the remaining string with the new string
        res = bluuuh + last_four
        result =int(res)
        

    prev=result
    
    fumm=str(result)
    summmy=fumm[:4] + '-' + fumm[4:6] + '-' + fumm[6:]
    
    
    bummy.loc[bummy.index[i], 'date'] = summmy
    
    
    if startDate <= result <= endDate:
        
        # Assuming df1 is the DataFrame you want to add a row to, and df2 is the DataFrame from which you want to add the row
        row_to_add = bummy.loc[i]  # Select the row you want to add from df2

        df1.loc[len(df1)] = row_to_add.values
        
        
        

# Ensure the 'date' column is in datetime format
#df1['date'] = pd.to_datetime(df1['date'])

# Ensure the 'date' column is in datetime format
df1['date'] = pd.to_datetime(df1['date'])






# Create a DataFrame with each president's first and last speech date
president_dates = df1.groupby('president')['date'].agg(['min', 'max']).reset_index()

president_dates.columns = ['president', 'first_speech', 'last_speech']



# Print the new DataFrame
print(president_dates)





# Create an empty DataFrame to store the counts
fumpy = pd.DataFrame(columns=['president'] + gents)

# Iterate over each president in df1
for president in df1['president'].unique():
    # Initialize a dictionary to store the counts for each president
    president_counts = {'president': president}
    # Filter df1 for speeches by the current president
    president_df = df1[df1['president'] == president]
    # Iterate over each word in gents
    for word in gents:
        # Count the occurrences of the word in the current president's speeches
        word_count = sum(president_df['transcript'].str.count(word))
        # Add the count to the dictionary
        president_counts[word] = word_count
    # Append the counts for the current president to fumpy
    fumpy = fumpy.append(president_counts, ignore_index=True)
    
    
# Set the 'president' column as the index
fumpy.set_index('president', inplace=True)

# Calculate the sum of each column and sort the columns by the sum in descending order
fumpy = fumpy.loc[:, fumpy.sum().sort_values(ascending=False).index]


republican_presidents_df = pd.DataFrame({
    "Name": [
        "Dwight D. Eisenhower",
        "Richard M. Nixon",
        "Gerald Ford",
        "Ronald Reagan",
        "George H. W. Bush"
    ]
})


# Print the DataFrame fumpy
print(fumpy)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  bummy.drop('index', axis=1, inplace=True)

              president first_speech last_speech
0  Dwight D. Eisenhower   1953-01-20  1961-01-17
1     George H. W. Bush   1988-08-18  1991-07-31
2           Gerald Ford   1974-08-09  1977-01-12
3       Harry S. Truman   1947-03-12  1953-01-15
4          Jimmy Carter   1976-09-23  1981-01-14
5       John F. Kennedy   1960-07-15  1963-10-26
6     Lyndon B. Johnson   1963-05-30  1969-01-14
7      Richard M. Nixon   1952-09-23  1974-08-09
8         Ronald Reagan   1964-10-27  1989-01-11
                     Soviet the Soviet Union Communist Communists Soviets  \
president                                                                   
Harry S. Truman          30               12        45         17       0   
Richard M. Nixon         43               25        45         15       5   
Dwight D. Eisenhower    103               34        51         13      17   
John F. Kennedy         159               85       178         79      17   
Lyndon B. Johnson       120               70       182         47       5   
Ronald Reagan           514              182        29          4     106   
Gerald Ford              21               18         3          0       0   
Jimmy Carter            161               95        14          3      12   
George H. W. Bush       137               71         2          0      11   

                     Russia Army Navy  
president                              
Harry S. Truman           7    2    1  
Richard M. Nixon          0    2    0  
Dwight D. Eisenhower     14    4    7  
John F. Kennedy          17   12    3  
Lyndon B. Johnson        30   24    9  
Ronald Reagan            23    7    8  
Gerald Ford               0    2    2  
Jimmy Carter             15    5    8  
George H. W. Bush         2    4    1

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)

from sklearn.preprocessing import StandardScaler, MinMaxScaler
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle

# Fill missing values with zeros
fumpy_filled = fumpy.fillna(0)

# Convert fumpy to numeric type
fumpy_numeric = fumpy_filled.apply(pd.to_numeric)





# Set the size of the heatmap
plt.figure(figsize=(15, 7))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_transposed, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 9  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx , y_pos_below, label, ha='center', va='center', fontsize=15, color=color, rotation=40)
    start_date, end_date = date_mapping[label]
    plt.text(idx+ .2, y_pos_below + .2, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
    
legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility

plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=130)
plt.ylabel('Entities', fontsize=20)
plt.title(f'Cold War Entity Frequency by Presidents \n {startyear} - {endyear} \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.3, "The heatmap displays the frequency of specific entities mentioned in President Speeches during the signifiacnt years of Cold War, with annotations indicating the start and end dates of of presidents in office during that time period starting with the announcement of the Truman Doctrine in 1947 and ends with Gorbachev resigning and what was left of the Soviet parliament voted to end itself. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)


plt.show()







# Initialize a StandardScaler
scaler = StandardScaler()

# Scale the data
fumpy_standardized = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)

# Set the size of the heatmap
plt.figure(figsize=(15, 7))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_standardized, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 9  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx , y_pos_below, label, ha='center', va='center', fontsize=15, color=color, rotation=40)
    start_date, end_date = date_mapping[label]
    plt.text(idx+ .2, y_pos_below + .2, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)

    
    
legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility   
    
    
    
plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=130)
plt.ylabel('Entities', fontsize=20)
plt.title(f'Cold War Entity Frequency by Presidents \n {startyear} - {endyear} Standardized \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.3, "The heatmap displays the Standardized frequency of specific entities mentioned in President Speeches during the signifiacnt years of Cold War, with annotations indicating the start and end dates of of presidents in office during that time period starting with the announcement of the Truman Doctrine in 1947 and ends with Gorbachev resigning and what was left of the Soviet parliament voted to end itself. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)


plt.show()








# Initialize a MinMaxScaler
scaler = MinMaxScaler()

# Scale the data
fumpy_scaled = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)

# Set the size of the heatmap
plt.figure(figsize=(15, 7))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_scaled, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 9  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx , y_pos_below, label, ha='center', va='center', fontsize=15, color=color, rotation=40)
    start_date, end_date = date_mapping[label]
    plt.text(idx+ .2, y_pos_below + .2, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)

    
legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility   
    
    

plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=130)
plt.ylabel('Entities', fontsize=20)
plt.title(f'Cold War Entity Frequency by Presidents \n {startyear} - {endyear} MinMax Scaled \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.3, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in President Speeches during the signifiacnt years of Cold War, with annotations indicating the start and end dates of of presidents in office during that time period starting with the announcement of the Truman Doctrine in 1947 and ends with Gorbachev resigning and what was left of the Soviet parliament voted to end itself. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)


plt.show()

samp_sorted= pd.read_csv('outputfinal_filename.csv')

startyear=1949
endyear=2023


startDate=19490816 #1949, August 16. Navy establishes Middle East Force. 
endDate=20230222 # Now, ealriest speach in data



gents = [
    "Iraq", "the Middle East", "Afghanistan", "Iran", 
    "Iraqi", "Taliban", "Afghan", "al Qaeda", 
    "Arab", "Saddam Hussein", "Saddam Hussein's", 
    "Iraqis", "Somalia", "Muslim", "Al Qaeda", 
    "Osama bin Laden", "Afghans", "Assad", "Lebanon", "Army", "Navy", 'Nato'
]










bummy = samp_sorted.loc[(samp_sorted['Year'] >= startyear)]
bummy.reset_index(inplace=True)
bummy.drop('index', axis=1, inplace=True)



df1 = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])


prev=startDate

for i in range(len(bummy)):
    date_string = bummy.loc[bummy.index[i], 'date']  # Accessing the 'date' column for each row
    cleaned_string = date_string.replace('-', '')
    
    # Convert string to number
    if 'T' in cleaned_string:
        cleaned_string=cleaned_string.split('T')[0]
    result = int(cleaned_string)
    if result<9999:
        
        original_str = str(prev)
        bluuuh = str(result)
    
        # Cut off the last four characters
        last_four = original_str[-4:]

        # Combine the remaining string with the new string
        res = bluuuh + last_four
        result =int(res)
        

    prev=result
    
    fumm=str(result)
    summmy=fumm[:4] + '-' + fumm[4:6] + '-' + fumm[6:]
    
    
    bummy.loc[bummy.index[i], 'date'] = summmy
    
    
    if startDate <= result <= endDate:
        
        # Assuming df1 is the DataFrame you want to add a row to, and df2 is the DataFrame from which you want to add the row
        row_to_add = bummy.loc[i]  # Select the row you want to add from df2

        df1.loc[len(df1)] = row_to_add.values
        
        
        
        
# Ensure the 'date' column is in datetime format
df1['date'] = pd.to_datetime(df1['date'])


# Create a DataFrame with each president's first and last speech date
president_dates = df1.groupby('president')['date'].agg(['min', 'max']).reset_index()
president_dates.columns = ['president', 'first_speech', 'last_speech']

# Print the new DataFrame
print(president_dates)





# Create an empty DataFrame to store the counts
fumpy = pd.DataFrame(columns=['president'] + gents)

# Iterate over each president in df1
for president in df1['president'].unique():
    # Initialize a dictionary to store the counts for each president
    president_counts = {'president': president}
    # Filter df1 for speeches by the current president
    president_df = df1[df1['president'] == president]
    # Iterate over each word in gents
    for word in gents:
        # Count the occurrences of the word in the current president's speeches
        word_count = sum(president_df['transcript'].str.count(word))
        # Add the count to the dictionary
        president_counts[word] = word_count
    # Append the counts for the current president to fumpy
    fumpy = fumpy.append(president_counts, ignore_index=True)
    
    
# Set the 'president' column as the index
fumpy.set_index('president', inplace=True)

# Calculate the sum of each column and sort the columns by the sum in descending order
fumpy = fumpy.loc[:, fumpy.sum().sort_values(ascending=False).index]


republican_presidents_df = pd.DataFrame({
    "Name": [
        "Donald Trump",
        "Dwight D. Eisenhower",
        "George H. W. Bush",
        "George W. Bush",
        "Gerald Ford",
        "Richard M. Nixon",
        "Ronald Reagan"
    ]
})

# Print the DataFrame fumpy
print(fumpy)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:31: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  bummy.drop('index', axis=1, inplace=True)

               president first_speech last_speech
0           Barack Obama   2008-08-28  2017-01-10
1           Bill Clinton   1993-01-20  2001-01-18
2           Donald Trump   2017-01-20  2021-01-20
3   Dwight D. Eisenhower   1953-01-20  1961-01-17
4      George H. W. Bush   1988-08-18  1993-01-05
5         George W. Bush   2001-01-20  2009-01-15
6            Gerald Ford   1974-08-09  1977-01-12
7        Harry S. Truman   1950-01-04  1953-01-15
8           Jimmy Carter   1976-09-23  1981-01-14
9              Joe Biden   2021-01-20  2023-02-22
10       John F. Kennedy   1960-07-15  1963-10-26
11     Lyndon B. Johnson   1963-05-30  1969-01-14
12      Richard M. Nixon   1952-09-23  1974-08-09
13         Ronald Reagan   1964-10-27  1989-01-11

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  fumpy = fumpy.append(president_counts, ignore_index=True)

                     Iraq Afghan Afghanistan the Middle East Iran Iraqi Arab  \
president                                                                      
Harry S. Truman         0      0           0               0    3     0    0   
Richard M. Nixon        0      0           0              15    1     0    5   
Dwight D. Eisenhower    3      0           0              34    5     0    6   
John F. Kennedy         1      0           0              16    1     1    1   
Lyndon B. Johnson       0      1           1              17    2     0   11   
Ronald Reagan           6     57          52              51   38     0   27   
Gerald Ford             0      0           0              13    0     0    2   
Jimmy Carter            6     22          22              45   65     0   45   
George H. W. Bush     115      3           3              23    6    23   42   
Bill Clinton           12      0           0              26    3     1    5   
George W. Bush        540     94          84              74   46   218   24   
Barack Obama          179    188         121              30   71    44   25   
Donald Trump           19     18          15              25  103     1   17   
Joe Biden              10    228         159               3    4     0    4   

                     Lebanon Army al Qaeda  ... Saddam Hussein Iraqis Somalia  \
president                                   ...                                 
Harry S. Truman            0    2        0  ...              0      0       0   
Richard M. Nixon           0    2        0  ...              0      0       0   
Dwight D. Eisenhower       2    4        0  ...              0      0       0   
John F. Kennedy            2   12        0  ...              0      0       0   
Lyndon B. Johnson          2   24        0  ...              0      0       0   
Ronald Reagan            134    7        0  ...              0      0       0   
Gerald Ford                0    2        0  ...              0      0       0   
Jimmy Carter               1    5        0  ...              0      0       0   
George H. W. Bush          3    9        0  ...             26      2      29   
Bill Clinton               1   13        0  ...              8      0      34   
George W. Bush            11   24       24  ...             55     61       1   
Barack Obama               2   10       88  ...              2     13       4   
Donald Trump               2   24        1  ...              0      0       2   
Joe Biden                  0    3       12  ...              0      0       3   

                     Navy Al Qaeda Assad Osama bin Laden Afghans Nato  \
president                                                               
Harry S. Truman         1        0     0               0       0    0   
Richard M. Nixon        0        0     0               0       0    0   
Dwight D. Eisenhower    7        0     0               0       0    0   
John F. Kennedy         3        0     0               0       0    0   
Lyndon B. Johnson       9        0     0               0       0    0   
Ronald Reagan           8        0     0               0       0    0   
Gerald Ford             2        0     0               0       0    0   
Jimmy Carter            8        0     0               0       0    0   
George H. W. Bush       3        0     0               0       0    0   
Bill Clinton            2        0     0               1       0    0   
George W. Bush          6       41     0               3       2    0   
Barack Obama            3        4    31              17       9   14   
Donald Trump           10        1     3               1       0    0   
Joe Biden               0        2     0               9      19    0   

                     Saddam Hussein's  
president                              
Harry S. Truman                     0  
Richard M. Nixon                    0  
Dwight D. Eisenhower                0  
John F. Kennedy                     0  
Lyndon B. Johnson                   0  
Ronald Reagan                       0  
Gerald Ford                         0  
Jimmy Carter                        0  
George H. W. Bush                   6  
Bill Clinton                        0  
George W. Bush                      7  
Barack Obama                        0  
Donald Trump                        0  
Joe Biden                           0  

[14 rows x 22 columns]

from sklearn.preprocessing import StandardScaler, MinMaxScaler
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle

# Fill missing values with zeros
fumpy_filled = fumpy.fillna(0)

# Convert fumpy to numeric type
fumpy_numeric = fumpy_filled.apply(pd.to_numeric)





# Set the size of the heatmap
plt.figure(figsize=(18, 10))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_transposed, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 25  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx-0.0, y_pos_below -0.5, label, ha='center', va='center', fontsize=16, color=color, rotation=40)
    start_date, end_date = date_mapping[label]
    plt.text(idx+0.1, y_pos_below -0.05, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)

    
legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility   
    
    
plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=135)
plt.ylabel('Entities', fontsize=15)
plt.title(f'Middle East Entity Frequency by Presidents \n {startyear} - {endyear} \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.15, "The heatmap displays the frequency of specific entities mentioned in President Speeches during the signifiacnt years of Middle East involvement, with annotations indicating the start and end dates of of presidents in office during that time period starts with the Navy establishing Middle East Force for first time and this period is still going on to this day so ends with the earliest speech in data. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)

plt.show()








# Initialize a StandardScaler
scaler = StandardScaler()

# Scale the data
fumpy_standardized = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)

# Set the size of the heatmap
plt.figure(figsize=(18, 10))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_standardized, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 25  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx-0.0, y_pos_below -0.5, label, ha='center', va='center', fontsize=16, color=color, rotation=40)
    start_date, end_date = date_mapping[label]
    plt.text(idx+0.1, y_pos_below -0.05, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)

legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility   
    
    
    
plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=135)
plt.ylabel('Entities', fontsize=15)
plt.title(f'Middle East Entity Frequency by Presidents \n {startyear} - {endyear} Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.15, "The heatmap displays the Standardized frequency of specific entities mentioned in President Speeches during the signifiacnt years of Middle East involvement, with annotations indicating the start and end dates of of presidents in office during that time period starts with the Navy establishing Middle East Force for first time and this period is still going on to this day so ends with the earliest speech in data. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)

plt.show()








# Initialize a MinMaxScaler
scaler = MinMaxScaler()

# Scale the data
fumpy_scaled = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)

# Set the size of the heatmap
plt.figure(figsize=(18, 10))  # You can adjust the size as needed

# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T

# Create the heatmap without annotations first
sns.heatmap(fumpy_scaled, annot=False, cmap='Blues')

# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
    for j in range(len(fumpy_transposed.columns)):
        plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)

        
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]


# Map the start and end dates for each president
date_mapping = {
    row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
    for _, row in president_dates.iterrows()
}


# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 25  # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
    plt.text(idx-0.0, y_pos_below -0.5, label, ha='center', va='center', fontsize=16, color=color, rotation=40)
    start_date, end_date = date_mapping[label]
    plt.text(idx+0.1, y_pos_below -0.05, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)

    
legend_elements = [
    Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
    Patch(facecolor='red', edgecolor='red', label='Republican')
]


plt.legend(handles=legend_elements, 
           loc='lower left', 
           bbox_to_anchor=(-0.3, -0.2),  # Coordinates for 'bbox_to_anchor' are relative to the axes
           title="Fontcolor of Presidents",
           fontsize='small',  # Adjust text size
           title_fontsize='medium',  # Adjust title font size
           frameon=True,  # Toggle the frame
           shadow=True)  # Add shadow for better visibility   
    
    
    
    
    
plt.xticks(ticks=[], labels=[])  # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)      
plt.xlabel('President', fontsize=20, labelpad=135)
plt.ylabel('Entities', fontsize=15)
plt.title(f'Middle East Entity Frequency by Presidents \n {startyear} - {endyear} MinMax Scaled \n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.15, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in President Speeches during the signifiacnt years of Middle East involvement, with annotations indicating the start and end dates of of presidents in office during that time period starts with the Navy establishing Middle East Force for first time and this period is still going on to this day so ends with the earliest speech in data. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)

plt.show()

'''
samp_sorted= pd.read_csv('outputfinal_filename.csv')

litz=samp_sorted.loc[:, ["Year", "transcript"]]
fr=litz["Year"].drop_duplicates()
dates=[]

for i in fr:
    dates.append(i)

# Create an empty DataFrame with specified columns
woah = pd.DataFrame(columns=['year', 'entity'])


# Load the spaCy model
nlp = spacy.load('en_core_web_sm')


# Function to extract entities
def extract_entities(text):
    doc = nlp(text)
    return [ent.text for ent in doc.ents if ent.label_ in ['GPE', 'ORG', 'PERSON', 'NORP', 'FAC', 'LOC', 'PRODUCT', 'EVENT', 'WORK_OF_ART', 'LAW', 'LANGUAGE']]

# Applying the function to each speech
all_entities = []

for i in dates:
    print(i)
    cur=litz.loc[litz["Year"]== i]
    cur.reset_index(inplace=True)
    cur.drop('index', axis=1, inplace=True)
    cur_entities = []
    
    for j in range(len(cur)):
        gum= cur.loc[j,'transcript']
        entities = extract_entities(gum)
        cur_entities.extend(entities)
        all_entities.extend(entities)
        
        
    entity_counts = Counter(cur_entities)
    for entity, count in entity_counts.items():
        fum= f"{entity}: {count}"
        woah.loc[len(woah)] = [ i,  fum]
        
entity2=[]
problem=[]
county=[]
for i in range(len(woah)):
    print(i)
    if woah.loc[i, 'entity'].count(':')==1:
        ayy=woah.loc[i, 'entity'].split(":")
        entity2.append(ayy[0])
        county.append(ayy[1])
    else:
        problem.append([woah.loc[i, :], i])


for i in range(len(problem)):
    index_to_remove2 = woah.index[problem[i][1]]
    woah = woah.drop(index_to_remove2)
    
woah['entity2']=entity2
woah['county']=county


woah.reset_index(inplace=True)
woah.drop('index', axis=1, inplace=True)
woah.to_csv('entities_spec.csv', index=False)
    
    
# Create a DataFrame with a single column named "enty" for the raw list of entites
df = pd.DataFrame(all_entities, columns=['enty'])
df.to_csv('entities_raw.csv', index=False)
'''

'\nsamp_sorted= pd.read_csv(\'outputfinal_filename.csv\')\n\nlitz=samp_sorted.loc[:, ["Year", "transcript"]]\nfr=litz["Year"].drop_duplicates()\ndates=[]\n\nfor i in fr:\n    dates.append(i)\n\n# Create an empty DataFrame with specified columns\nwoah = pd.DataFrame(columns=[\'year\', \'entity\'])\n\n\n# Load the spaCy model\nnlp = spacy.load(\'en_core_web_sm\')\n\n\n# Function to extract entities\ndef extract_entities(text):\n    doc = nlp(text)\n    return [ent.text for ent in doc.ents if ent.label_ in [\'GPE\', \'ORG\', \'PERSON\', \'NORP\', \'FAC\', \'LOC\', \'PRODUCT\', \'EVENT\', \'WORK_OF_ART\', \'LAW\', \'LANGUAGE\']]\n\n# Applying the function to each speech\nall_entities = []\n\nfor i in dates:\n    print(i)\n    cur=litz.loc[litz["Year"]== i]\n    cur.reset_index(inplace=True)\n    cur.drop(\'index\', axis=1, inplace=True)\n    cur_entities = []\n    \n    for j in range(len(cur)):\n        gum= cur.loc[j,\'transcript\']\n        entities = extract_entities(gum)\n        cur_entities.extend(entities)\n        all_entities.extend(entities)\n        \n        \n    entity_counts = Counter(cur_entities)\n    for entity, count in entity_counts.items():\n        fum= f"{entity}: {count}"\n        woah.loc[len(woah)] = [ i,  fum]\n        \nentity2=[]\nproblem=[]\ncounty=[]\nfor i in range(len(woah)):\n    print(i)\n    if woah.loc[i, \'entity\'].count(\':\')==1:\n        ayy=woah.loc[i, \'entity\'].split(":")\n        entity2.append(ayy[0])\n        county.append(ayy[1])\n    else:\n        problem.append([woah.loc[i, :], i])\n\n\nfor i in range(len(problem)):\n    index_to_remove2 = woah.index[problem[i][1]]\n    woah = woah.drop(index_to_remove2)\n    \nwoah[\'entity2\']=entity2\nwoah[\'county\']=county\n\n\nwoah.reset_index(inplace=True)\nwoah.drop(\'index\', axis=1, inplace=True)\nwoah.to_csv(\'entities_spec.csv\', index=False)\n    \n    \n# Create a DataFrame with a single column named "enty" for the raw list of entites\ndf = pd.DataFrame(all_entities, columns=[\'enty\'])\ndf.to_csv(\'entities_raw.csv\', index=False)\n'

woah = pd.read_csv('entities_spec.csv')
woah

df = pd.read_csv("entities_raw.csv")

# Extract the "col1" column as a list of strings
all_entities = df['enty'].tolist()

entity_counts = Counter(all_entities)
allofem=[]
for entity, count in entity_counts.items():
        fum= f"{entity}: {count}"
        allofem.append(fum)

# Using most_common method
ordered_counts = entity_counts.most_common()
ents=[]
for entity, count in ordered_counts:
    print(f"{entity}: {count}")
    en= [entity,count ]
    ents.append(en)

the United States: 8285
Congress: 8163
American: 6278
America: 4351
Americans: 2724
State: 2275
States: 1998
Constitution: 1873
Government: 1583
Senate: 1572
Treasury: 1481
Washington: 1156
Europe: 1037
Mexico: 978
China: 841
Navy: 777
Army: 760
Great Britain: 679
Cuba: 618
the Soviet Union: 613
British: 603
Iraq: 590
Spain: 587
Indian: 571
France: 520
the Federal Government: 516
Indians: 506
Texas: 497
Vietnam: 473
House: 472
Russia: 467
Afghanistan: 458
Soviet: 450
Executive: 442
the House of Representatives: 434
Republican: 430
Republicans: 427
the United Nations: 407
United States: 399
Democrats: 395
Department: 387
Japan: 387
Germany: 386
The United States: 382
Asia: 380
the United States of America: 367
European: 364
the Middle East: 363
Chinese: 361
Pacific: 357
Communist: 354
Israel: 332
California: 327
Africa: 323
Territory: 323
New York: 318
Iran: 282
the White House: 280
U.S.: 277
Kennedy: 276
God: 274
Democratic: 270
Panama: 261
Medicare: 252
Atlantic: 251
Missouri: 248
NATO: 247
the Supreme Court: 245
Mississippi: 243
Nation: 240
the General Government: 235
West: 233
French: 233
Earth: 231
Berlin: 226
South: 224
Nicaragua: 224
Mexican: 223
Commission: 223
Spanish: 217
Canada: 215
Administration: 215
German: 212
Carter: 196
Social Security: 196
Cabinet: 195
Paris: 194
Iraqi: 191
Ford: 190
the District of Columbia: 189
Nebraska: 189
House of Representatives: 184
Louisiana: 181
Florida: 177
Russian: 177
Interior: 177
Soviets: 177
Communists: 175
Cuban: 174
Korea: 174
Q.: 174
Latin America: 170
Kansas: 167
Ohio: 166
South Vietnam: 165
Central America: 163
Italy: 163
Bush: 163
Isthmus: 160
Philippines: 160
Territories: 159
Lebanon: 159
Poland: 159
Japanese: 157
World War II: 157
Laughter: 156
the Postmaster-General: 154
Federal: 153
London: 149
Reagan: 149
the National Government: 147
Nixon: 147
republican: 145
Brazil: 145
New Mexico: 145
Alaska: 145
Viet-Nam: 145
Congressional: 139
Virginia: 138
Ukraine: 137
Turkey: 134
North: 133
the War Department: 128
Oregon: 128
South Carolina: 126
New Orleans: 125
Governments: 124
East: 124
Geneva: 123
Mondale: 123
Speaker: 122
England: 122
English: 121
Pennsylvania: 120
Georgia: 120
Taliban: 118
the Constitution of the United States: 116
Venezuela: 116
the Department of State: 115
Departments: 115
Eisenhower: 115
Greece: 113
Hawaii: 112
South Vietnamese: 112
Afghan: 112
Alabama: 111
South Viet-Nam: 111
North Vietnam: 111
Democrat: 110
Illinois: 109
the British Government: 108
Hanoi: 108
African: 107
Gorbachev: 106
al Qaeda: 106
Utah: 105
the Treasury Department: 104
Chile: 103
the Mexican Government: 100
Arab: 99
Clinton: 98
the Congress of the United States: 95
the Post-Office Department: 95
Southeast Asia: 94
India: 92
the Democratic Party: 92
San Francisco: 91
Chicago: 91
Trump: 91
the Bank of the United States: 90
the Republican Party: 90
Western Europe: 90
Peru: 89
Egypt: 89
Republics: 87
Jefferson: 87
Austria: 86
Moscow: 86
Massachusetts: 85
Khrushchev: 85
earth: 84
South America: 84
Negro: 84
Colombia: 83
U.N.: 83
Legislature: 82
Philadelphia: 81
the Western Hemisphere: 81
Pakistan: 81
Dole: 80
Defense: 80
Kuwait: 80
War: 79
Portugal: 79
Stanton: 79
Houses of Congress: 78
Americas: 78
Clay: 77
Providence: 76
Arkansas: 76
Court: 76
Houses: 75
Britain: 75
Philippine: 75
Saddam Hussein: 75
Iraqis: 75
the Department of Justice: 74
Truman: 74
Lincoln: 73
the Panama Canal: 73
Watergate: 73
Somalia: 73
Mediterranean: 72
Porto Rico: 72
D.C.: 72
SMITH: 72
Bosnia: 72
Arizona: 71
Medicaid: 71
Tennessee: 70
Capitol: 70
the Rio Grande: 70
Honduras: 70
Palestinians: 70
Puerto Rico: 69
the Department of Agriculture: 69
Syria: 69
the Spanish Government: 68
the Dominican Republic: 68
Cambodia: 68
Palestinian: 68
Biden: 68
North Carolina: 67
Johnson: 67
Gulf: 67
Boston: 66
Denmark: 66
Belgium: 66
the Interstate Commerce Commission: 66
South Korea: 66
’s: 66
North Korea: 65
Detroit: 64
Republic: 64
the French Government: 64
Canadian: 64
Putin: 64
Agriculture: 63
Maine: 63
Michigan: 63
the Southern States: 63
North Vietnamese: 63
Roosevelt: 62
Eastern Europe: 62
Israelis: 62
COVID-19: 62
Baltimore: 61
Lehrer: 61
the State Department: 60
Douglas: 60
Nazi: 60
AUDIENCE: 60
Abraham Lincoln: 59
Grant: 59
Polish: 58
Israeli: 58
congress: 57
West Point: 57
Iowa: 57
Castro: 57
New Jersey: 56
Southern: 56
the Civil War: 56
Hitler: 56
KENNEDY: 56
Laos: 56
Perot: 56
Kentucky: 55
Havana: 55
CARTER: 55
APPLAUSE: 55
Guatemala: 54
Australia: 54
Russians: 54
White House: 54
REAGAN: 54
John: 53
Manila: 53
Sweden: 52
Indiana: 52
Madrid: 52
the United States Government: 52
Italian: 52
Iranian: 52
the Rocky Mountains: 51
Hungary: 51
Governors: 51
Nancy: 51
USA: 51
Representatives: 50
George Washington: 50
Christian: 50
Monroe: 50
Germans: 50
the Republic of Panama: 50
Yugoslavia: 50
Middle East: 50
ISIS: 50
Colorado: 49
FBI: 49
NIXON: 49
Selma: 49
Joe Biden: 49
Maryland: 48
fidelity: 48
the Gulf of Mexico: 48
the Chinese Government: 48
the United Kingdom: 48
Budget: 48
Vietnamese: 48
Mike: 48
Muslim: 48
the Federal Constitution: 47
Cubans: 47
Peking: 47
Nazis: 47
United Nations: 47
The Soviet Union: 47
McNamara: 47
DOLE: 47
the Pacific Ocean: 46
Grenada: 46
the Declaration of Independence: 46
Asian: 46
Saudi Arabia: 46
El Salvador: 46
Rome: 45
democratic: 45
Costa Rica: 45
Honolulu: 45
the Far East: 45
the Oval Office: 45
CLINTON: 45
Al Qaeda: 45
Revolution: 44
St. Louis: 44
Vienna: 44
West Berlin: 44
Steve: 44
Kosovo: 44
CDC: 44
Bolivia: 43
New York City: 43
Labor: 43
Pearl Harbor: 43
Indochina: 43
Muslims: 43
National: 42
South American: 42
Netherlands: 42
The Federal Government: 42
Caribbean: 42
North Viet-Nam: 42
Baghdad: 42
the Isthmus of Panama: 41
the Navy Department: 41
Chamber: 41
New Hampshire: 41
Thomas Jefferson: 41
Formosa: 41
Armed Forces: 41
Connecticut: 40
Eastern: 40
Minnesota: 40
Chilean: 40
the National Guard: 40
Filipinos: 40
The United Nations: 40
Franklin Roosevelt: 40
the Cold War: 40
Walters: 40
NAFTA: 40
TRUMP: 40
Jackson: 39
Hon: 39
Liberia: 39
the Missouri Compromise: 39
TELEGRAM: 39
Colon: 39
Obama: 39
Prussia: 38
Paraguay: 38
Norway: 38
Ecuador: 38
the Provisional Government: 38
Beirut: 38
Jewish: 38
North Africa: 38
Pentagon: 38
BUSH: 38
DUKAKIS: 38
Justice: 37
Western: 37
the West Indies: 37
the Regular Army: 37
Rhode Island: 37
Bureau: 37
Oklahoma: 37
Haiti: 37
the Joint Chiefs of Staff: 37
John F. Kennedy: 37
Ireland: 36
the Mexican Republic: 36
World War: 36
Kremlin: 36
Ukrainian: 36
NASA: 36
Islam: 36
Fellow-Citizens of the Senate: 35
Parliament: 35
Hawaiian: 35
the State of Texas: 35
Rusk: 35
Hay: 35
Libya: 35
Saddam: 35
North American: 34
Wisconsin: 34
Switzerland: 34
the Revised Statutes: 34
Jews: 34
CIA: 34
Westmoreland: 34
African American: 34
Treaty: 33
Supreme Court: 33
Board: 33
North America: 33
Jim: 33
the People's Republic of China: 33
FDA: 33
Cherokees: 32
Union: 32
the General Land Office: 32
Chambers: 32
the Persian Gulf: 32
Angola: 32
George Bush: 32
the Independence of the United States: 31
the Government of Spain: 31
New Zealand: 31
Santiago: 31
Cairo: 31
Marshall: 31
Martin Luther King: 31
Fauci: 31
Potomac: 30
the Argentine Republic: 30
Colombian: 30
Bible: 30
Congo: 30
Woodrow Wilson: 30
Colonies: 30
Latin American: 30
NEWMAN: 30
Brady: 30
Osama bin Laden: 30
Afghans: 30
Assad: 30
us: 29
King: 29
Brussels: 29
Taylor: 29
I.: 29
Allies: 29
Ronald Reagan: 29
Harry Truman: 29
Vietcong: 29
the Mississippi River: 28
Catholic: 28
the American Government: 28
The Hague: 28
Tokyo: 28
Los Angeles: 28
Allied: 28
the World War: 28
Marines: 28
OAS: 28
Birx: 28
Columbia: 27
Delaware: 27
Empire: 27
Executive Departments: 27
Mexicans: 27
San Juan: 27
West Virginia: 27
Cleveland: 27
Taft: 27
Iceland: 27
Burma: 27
Air Force: 27
the Free World: 27
Quemoy: 27
the Great Depression: 27
Hillary: 27
Commerce: 26
Assembly: 26
navy: 26
Charleston: 26
the Great Lakes: 26
Greek: 26
The United States of America: 26
Madison: 26
Presidential: 26
the Interior Department: 26
Valparaiso: 26
McKinley: 26
Lodge: 26
South Africa: 26
Palestine: 26
Indonesia: 26
Brezhnev: 26
SALT: 26
Hispanic: 26
Mitch: 26
Divine Providence: 25
Britannic Majesty: 25
Christians: 25
the National Treasury: 25
the Old World: 25
Kingdom: 25
West India: 25
Texan: 25
Negroes: 25
the Pension Bureau: 25
SEC: 25
Dominion: 25
Italians: 25
Churchill: 25
the Second World War: 25
the Armed Forces: 25
the Defense Department: 25
Matsu: 25
Saigon: 25
OPEC: 25
MONDALE: 25
Northern Ireland: 25
Holland: 24
Commissioners: 24
the Patent Office: 24
Indian Affairs: 24
the City of Mexico: 24
Atlanta: 24
Transportation: 24
the Department of Commerce: 24
Czechoslovakia: 24
Irish: 24
Winston Churchill: 24
Yalta: 24
the United Nations Charter: 24
the State of the Union: 24
Joe: 24
Taiwan: 24
Richard Nixon: 24
the Peace Corps: 24
Yemen: 24
Lebanese: 24
John McCain: 24
Imperial: 23
Bogota: 23
the Chinese Empire: 23
the War of 1812: 23
the Republic of Mexico: 23
Central American: 23
Topeka: 23
Montana: 23
The Department of Agriculture: 23
Samoan: 23
Bulgaria: 23
Kansas City: 23
the Federal Reserve Board: 23
REYNOLDS: 23
Jordan: 23
Tony: 23
Michelle: 23
these United States: 22
the National Legislature: 22
Brown: 22
Lakes: 22
Council: 22
Africans: 22
Bill: 22
US: 22
Convention: 22
the Pacific Coast: 22
Nicaraguan: 22
Salvador: 22
Dingley: 22
Theodore Roosevelt: 22
Singapore: 22
inter-American: 22
Goldwater: 22
Kissinger: 22
Syrian: 22
FEMA: 22
Nancy Pelosi: 22
Barrett: 22
Revolutionary: 21
the Republic of Colombia: 21
the States of the Union: 21
the Imperial Government: 21
Henry Clay: 21
the Republican party: 21
the United States Army: 21
the Dominion of Canada: 21
Samoa: 21
the Tariff Commission: 21
Wilson: 21
The National Government: 21
NAVY: 21
Canal: 21
Tunisia: 21
Warsaw: 21
the Security Council: 21
Jerusalem: 21
John Kennedy: 21
Arabs: 21
Gaza: 21
Newtown: 21
Xi: 21
the State of: 20
Lake Erie: 20
Morocco: 20
Confederation: 20
Majesty: 20
the State of New York: 20
Turkish: 20
colonies: 20
Federal Government: 20
the New World: 20
Baker: 20
the Government of Chile: 20
Omaha: 20
the German Government: 20
Alaskan: 20
the Bering Sea: 20
the Republic of Cuba: 20
Filipino: 20
the Department of Commerce and Labor: 20
the Near East: 20
the United Nations Security Council: 20
Camp David: 20
Capitol Hill: 20
Cyprus: 20
Islamic: 20
Border Patrol: 20
Google: 20
COVID: 20
Nations: 19
Session: 19
Constitutional: 19
Academy: 19
the Post Office Department: 19
Asiatic: 19
Andrew Jackson: 19
the Marine Corps: 19
Ryan: 19
Paredes: 19
Central and South America: 19
The Supreme Court: 19
the Department of the Interior: 19

Annapolis: 19
North and South: 19
Teheran: 19
Young: 19
Shanghai: 19
San Domingo: 19
Burlingame: 19
Ehrman: 19
Dominican: 19
the Imperial German Government: 19
Serbia: 19
Ethiopia: 19
Thailand: 19
Ghana: 19
Robert Kennedy: 19
Houston: 19
the Justice Department: 19
Bob Dole: 19
SALT II: 19
Barbara: 19
bin Laden: 19
Jill: 19
Fellow Citizens: 18
Country: 18
the Government of the United States: 18
the French Republic: 18
John Adams: 18
Time: 18
Sabine: 18
Chesapeake: 18
Buenos Ayres: 18
Rio de Janeiro: 18
the Postmaster General: 18
Brazilian: 18
Commonwealth: 18
the Federal Treasury: 18
the State of Virginia: 18
Vera Cruz: 18
Guadalupe Hidalgo: 18
Korean: 18
Domingo: 18
Kellogg: 18
Yale: 18
MacArthur: 18
Lithuania: 18
the Air Force: 18
Franklin Delano Roosevelt: 18
Welfare: 18
the World Bank: 18
the Organization of American States: 18
Alliance: 18
Gore: 18
Thieu: 18
VA: 18
AmeriCorps: 18
Rwanda: 18
Mitch McConnell: 18
Citizens: 17
Tripoli: 17
Northwest: 17
Cumberland: 17
the German Empire: 17
Scott: 17
San Diego: 17
Harris: 17
the Chilean Government: 17
The Republican Party: 17
PANAMA: 17
Marine: 17
Normandy: 17
Harry S. Truman: 17
the General Assembly: 17
Stalin: 17
the Department of Defense: 17
Harvard: 17
de Gaulle: 17
Bundy: 17
the Great Society: 17
Asians: 17
the Berlin Wall: 17
Helsinki: 17
MS: 17
the West Bank: 17
the European Union: 17
n’t: 17
Al Qaida: 17
Abbott: 17
Mint: 16
the Independence of the United States of America: 16
the Department of War: 16
Christianity: 16
Administrations: 16
Belgian: 16
Siam: 16
Stevenson: 16
Europeans: 16
Tehuantepec: 16
North and South America: 16
Marine Corps: 16
the Northern States: 16
Lecompton: 16
New England: 16
Nevada: 16
Sherman: 16
the War of the Rebellion: 16
Argentine: 16
North Dakota: 16
Queen: 16
George: 16
Pittsburgh: 16
the Federal Reserve System: 16
the Border Patrol: 16
the Reconstruction Finance Corporation: 16
Tonight: 16
Sicily: 16
the National Security Council: 16
Red China: 16
the cold war: 16
Romney: 16
Kevin: 16
Kabul: 16
U.S.A.: 16
INF: 16
UK: 16
Second Amendment: 16
Matt: 16
Soleimani: 16
BRYAN: 16
Catholic Majesty: 15
Nashville: 15
Portuguese: 15
Lee: 15
the State of Maine: 15
the west coast: 15
the State of South Carolina: 15
Hamilton: 15
the Executive Departments: 15
the Pension Office: 15
the Government of Mexico: 15
New Granada: 15
Mormons: 15
the United States Navy: 15
the Indian Territory: 15
the Civil Service Commission: 15
Ross: 15
National Guard: 15
Argentina: 15
the Red Cross: 15
Orient: 15
the Gulf Coast: 15
Tribunal: 15
the United States Supreme Court: 15
Balkans: 15
cooperatives: 15
the Federal Reserve: 15
Chief Justice: 15
the Republic of Korea: 15
U.S.S.R.: 15
the Arab States: 15
ABC News: 15
Mars: 15
Peace Corps: 15
Soviet-American: 15
the Voting Rights Act: 15
Head Start: 15
Notre Dame: 15
Amy: 15
African-American: 15
African-Americans: 15
Native American: 15
Hamas: 15
Jesse: 15
Apple: 15
CNN: 15
Greenwood: 15
fed: 14
the Government of Great Britain: 14
St. Petersburg: 14
Chief Magistrate: 14
the Articles of Confederation: 14
Delegate: 14
Mark: 14
the Southwest Branch: 14
Caracas: 14
El Paso: 14
the State of Tennessee: 14
the Territory of Kansas: 14
Persia: 14
The Postmaster-General: 14
Fort Sumter: 14
the Indian Bureau: 14
Gettysburg: 14
Idaho: 14
Revised Statutes: 14
Civil War: 14
Concord: 14
Egan: 14
the Suez Canal: 14
Guam: 14
Manchuria: 14
Huerta: 14
the League of Nations: 14
Chiang Kai-shek: 14
Poles: 14
Bryan: 14
the Soviet Government: 14
GI: 14
Dwight Eisenhower: 14
South Asia: 14
the Alliance for Progress: 14
Peter: 14
the U.N. Security Council: 14
Mideast: 14
PLO: 14
Wieghart: 14
Cheney: 14
Sonia: 14
al Qaeda’s: 14
Natoma: 14
ISIL: 14
the World Trade Organization: 14
MS-13: 14
Donald Trump: 14
ICE: 14
Hitherto: 13
Creeks: 13
James: 13
Sovereign: 13
Lawrence: 13
the Government of France: 13
Buenos Aires: 13
Naples: 13
Buffalo: 13
the Lake of the Woods: 13
Caroline: 13
Pinckney: 13
ANDREW JOHNSON: 13
Central: 13
the Caribbean Sea: 13
Uruguay: 13
the Colombian Government: 13
Kongo: 13
the Hawaiian Islands: 13
Malmros: 13
Hemisphere: 13
Dwight D. Eisenhower: 13
Committee: 13
the League of Women Voters: 13
Pat: 13
the Truman Administration: 13
Fulbright: 13
East Germany: 13
the Common Market: 13
Wallace: 13
Hollywood: 13
Air Force One: 13
START: 13
Main Street: 13
Sawyer: 13
Qadhafi: 13
the Joint Chiefs: 13
Saddam Hussein's: 13
John Boehner: 13
the Post-Office: 12
UnitedStates: 12
Powers: 12
Franklin: 12
senate: 12
Norfolk: 12
the Military Academy: 12
the Treaty of Ghent: 12
Pensacola: 12
Canton: 12
Columbus: 12
Austrian: 12
Canadians: 12
the Republic of Texas: 12
Islands: 12
Christendom: 12
the American Republics: 12
Lima: 12
Regular Army: 12
the Pacific coast: 12
Lexington: 12
the Wilmot Proviso: 12
Webster: 12
A Proclamation
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the Netherlands: 12
Venezuelan: 12
section 3: 12
Yorktown: 12
LOOMIS: 12
the Bureau of Corporations: 12
Finland: 12
the Canal Zone: 12
Payne: 12
Rumania: 12
the Coast Guard: 12
the American Nation: 12
the Farm Board: 12
the Democratic House of Representatives: 12
the Export-Import Bank: 12
General de Gaulle: 12
NATO Allies: 12
SEATO: 12
the Atlantic Alliance: 12
A-11: 12
Kosygin: 12
the Korean Peninsula: 12
Sinai: 12
SS-20: 12
Kraft: 12
Moon: 12
Tucson: 12
Washington State: 12
Noriega: 12
Audience: 12
David: 12
Gabby: 12
Alan: 12
Laughs: 12
DR: 12
Alexandria: 11
Fellow-Citizens: 11
Cherokee: 11
the United   States: 11
Algiers: 11
Tunis: 11
republics: 11
Barbary: 11
Spaniards: 11
the Secretaries of War: 11
Sultan: 11
Porte: 11
Territorial: 11
Granada: 11
the British Provinces: 11
the American Union: 11
Halls Stream: 11
Collins: 11
the Oregon Territory: 11
the Del Norte: 11
Dutch: 11
the Naval Academy: 11
Amazon: 11
Ann: 11
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the Italian Government: 11
NAVY DEPARTMENT: 11
Egyptian: 11
Jamestown: 11
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Bering Sea: 11
Latin-American: 11
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un-American: 11
the Philippine Islands: 11
Loomis: 11
The Republic of Panama: 11
the Great War: 11
the Holy Land: 11
the Reconstruction Corporation: 11
Tehran: 11
Franklin D. Roosevelt: 11
the International Monetary Fund: 11
Mitchell: 11
Joshua: 11
the United Nations General Assembly: 11
Polaris: 11
Marxist: 11
the Voice of America: 11
Klan: 11
University: 11
Goldberg: 11
Miami: 11
Robert Frost: 11
Thomas: 11
the Civil Rights Act: 11
North Korean: 11
Trewhitt: 11
KRAFT: 11
Libyan: 11
Boeing: 11
Barnes: 11
Olympics: 11
Armageddon: 11
Washington, DC: 11
Dukakis: 11
Latino: 11
McCain: 11
African Americans: 11
Tom: 11
HHS: 11
It&#39;s: 11
Obamacare: 11
Maduro: 11
Doug: 11
Roche: 11
Wuhan: 11
Jon: 11
Universe: 10
Niagara: 10
Law: 10
the Atlantic States: 10
The House of Representatives: 10
The British Government: 10
Church: 10
concord: 10
Imperial Majesty: 10
the French Revolution: 10
Portland: 10
The Constitution of the United States: 10
Newfoundland: 10
Southwest: 10
the Western States: 10
Santa Fe: 10
Georgetown: 10
Smith: 10
Cincinnati: 10
Liverpool: 10
the Federal Union: 10
the States and Territories: 10
Vermont: 10
the supreme court: 10
Graham: 10
the Northwest Branch: 10
Richmond: 10
the Atlantic Ocean: 10
the Hudsons Bay Company: 10
Californias: 10
Amistad: 10
Polk: 10
Valley: 10
Key West: 10
the Northwestern Territory: 10
Hungarian: 10
Missouri Compromise: 10
the moon: 10
Jamaica: 10
Powell: 10
Stevens: 10
the Judiciary Committee: 10
Confederate: 10
WAR DEPARTMENT: 10
Adjutant: 10
the General of the Army: 10
San Antonio: 10
the South Pacific: 10
St. Paul: 10
the Bureau of Education: 10
the Court of Claims: 10
the Smithsonian Institution: 10
Wyoming: 10
the Indian Ocean: 10
section 6: 10
Apia: 10
the Governments of the United States: 10
British Columbia: 10
Oklahoma City: 10
the Democratic party: 10
the Agricultural Department: 10
Armenia: 10
Guantanamo Bay: 10
the Department of Labor: 10
the Secret Service: 10
the Emancipation Proclamation: 10
Tampico: 10
Herbert Hoover: 10
the Tennessee Valley: 10
the Democratic National Committee: 10
Hoover: 10
Party: 10
Malaya: 10
Mussolini: 10
the Rules Committee: 10
the Korean War: 10
International Communism: 10
Free World: 10
Humphrey: 10
Vanocur: 10
Dean: 10
non-Communist: 10
the World Health Organization: 10
John Fitzgerald Kennedy: 10
the Republican Convention: 10
Eugene Black: 10
Bunker: 10
U.S.-Soviet: 10
the Vietnam war: 10
World War I: 10
Sam Rayburn: 10
Security Council: 10
John Dean: 10
Shultz: 10
Haldeman: 10
Independents: 10
DREW: 10
Solidarity: 10
Sandinistas: 10
Beijing: 10

Soviet Union: 10
Desert Storm: 10
Somalis: 10
Milosevic: 10
Scout: 10
Sunni: 10
Boy Scouts: 10
Mike Pence: 10
Navigation: 9
Tribes: 9
St. Croix: 9
THE UNITED STATES: 9
Savannah: 9
Java: 9
Lewis: 9
Britannic Majesty's: 9
Province: 9
Lisbon: 9
the Black Sea: 9
Greeks: 9
the Columbia River: 9
the Ohio River: 9
Houses of the Congress: 9
Bank of the United States: 9
the Eastern States: 9
Department of the Government: 9
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John Quincy Adams: 9
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the Detroit River: 9
European Governments: 9
the Hawaiian Government: 9
the Union of the States: 9
Prussian: 9
the War and Navy Departments: 9
Napoleon: 9
American Republics: 9
the Republic of Nicaragua: 9
Independence: 9
the Panama Railroad Company: 9
the Pacific Railroad: 9
Johnston: 9
Peck: 9
Anderson: 9
the United States of Colombia: 9
Engineers: 9
the Pacific States: 9
the Freedmen's Bureau: 9
Brownsville: 9
HAMILTON: 9
section 4: 9
Berne: 9
Hebrew: 9
the Government of Italy: 9
Acapulco: 9
a United States: 9
Itata: 9
Schley: 9
Jr.: 9
Guantanamo: 9
the Dominion Government: 9
JOHN HAY: 9
the Customs Tariff of the Republic of Cuba: 9
Cartagena: 9
Clark: 9
The Nation: 9
Reynolds: 9
Bishop: 9
Ottawa: 9
The Red Cross: 9
Albania: 9
the Federal Reserve Act: 9
the Shipping Board: 9
the Bill of Rights: 9
Serbs: 9
Veterans: 9
the Senate Foreign Relations Committee: 9
Des Moines: 9
the Recovery Act: 9
Nazism: 9
General Eisenhower: 9
Harriman: 9
Crimea: 9
Iwo Jima: 9
the Veterans Administration: 9
Mayor: 9
West Germany: 9
the Defense Production Act: 9
the Boy Scouts: 9
Dulles: 9
Health, Education: 9
Lyndon Johnson: 9
Scranton: 9
Macmillan: 9
Nigeria: 9
Buy American: 9
the Commerce Department: 9
Paul: 9
the Rose Garden: 9
Johns Hopkins: 9
Midwest: 9
Education: 9
Romania: 9
the Democratic Republic of Vietnam: 9
Abrams: 9
Jimmy Carter: 9
Middle Eastern: 9
the Burger Court: 9
Hispanics: 9
ABM: 9
the Congressional Budget Office: 9
Grace: 9
KONDRACKE: 9
Health and Human Services: 9
Bahrain: 9
Colin Powell: 9
GATT: 9
Pakistani: 9
Serbian: 9
Sasha: 9
Ben: 9
Sotomayor: 9
Kim: 9
Asian American: 9
Mnuchin: 9
World War Two: 9
Sonny: 9
Deborah: 9
BIRX: 9
N95: 9
Tulsa: 9
Zelenskyy: 9
the Post Office: 8
andWhereas: 8
premises: 8
the Supreme Ruler: 8
Montgomery: 8
Floridas: 8
Warren: 8
Alexander: 8
the States of Georgia: 8
the American Navy: 8
the Territories of the United States: 8
Louisville: 8
the Ordnance Department: 8
Bermuda: 8
the Sublime Porte: 8
the American Revolution: 8
Supreme: 8
the United States Bank: 8
the Mexican Congress: 8
Farewell Address: 8
Bolivar: 8
Anthony: 8
the organic law: 8
St. John: 8
the Republic of New Granada: 8
Adams: 8
Santa Anna: 8
Catholics: 8
the State of Nicaragua: 8
the Mexican War: 8
the Turkish Government: 8
West Indian: 8
Pacific States: 8
Independence Hall: 8
Springfield: 8
the Empire of Japan: 8
Baltic: 8
the organic act: 8
the State of Panama: 8
Clayton: 8
Walker: 8
the Territory of Utah: 8
Washington Territory: 8
San Jose: 8
Brooklyn: 8
The District of Columbia: 8
Va.: 8
Article II: 8
the Revolutionary War: 8
Australian: 8
Indian Territory: 8
the Japanese Government: 8
Mount Vernon: 8
the Railway Mail Service: 8
The United States Government: 8
Sec: 8
Sydney: 8
the Geological Survey: 8
The War Department: 8
Chinaman: 8
Newark: 8
Trenton: 8
South Dakota: 8
the Independent State: 8
Vancouver: 8
Czar: 8
Riggin: 8
Blaine: 8
Diaz: 8
Negros: 8
the National Capital: 8
Buenaventura: 8
the Reclamation Service: 8
Benson: 8
the Forest Service: 8
the Nation: 8
the Middle West: 8
Muscle Shoals: 8
Coolidge: 8
Bank: 8
Social Security Act: 8
the Executive Branch: 8
Hitlerism: 8
North Atlantic: 8
Tom Paine: 8
Fascist: 8
Coast Guard: 8
the General Assembly of the United Nations: 8
the United Nations Relief and Rehabilitation Administration: 8
Julie: 8
communist: 8
The Middle East: 8
Hubert Humphrey: 8
Richard: 8
NBC News: 8
Rockefeller: 8
U-2: 8
SHADEL: 8
Pescadores: 8
the Marshall Plan: 8
UN: 8
Olympic: 8
East-West: 8
Malaysia: 8
Appalachia: 8
Hilsman: 8
Panamanian: 8
the Panama Canal Zone: 8
Lop Nor: 8
the East Room: 8
Vance: 8
the West Coast: 8
the Republic of Vietnam: 8
NLF: 8
Gerald Ford: 8
the Special Prosecutor: 8
Frankel: 8
FRANKEL: 8
Rabin: 8
Rhodesia: 8
Maynard: 8
B-1: 8
Warnke: 8
Persian Gulf: 8
Teddy Roosevelt: 8
MX: 8
Omaha Beach: 8
General Motors: 8
EPA: 8
KALB: 8
Holocaust: 8
Jim Baker: 8
Reykjavik: 8
John Mashek: 8
Saudi: 8
Kuwaiti: 8
Byelorussia: 8
the Iraq war: 8
Somali: 8
Belfast: 8
Kassebaum: 8
George W. Bush: 8
U-S-A: 8
Boehner: 8
Shi'a: 8
the Department of Homeland Security: 8
Homeland Security: 8
Malia: 8
Chuck: 8
the Affordable Care Act: 8
Asian Americans: 8
Walmart: 8
Pence: 8
Kevin McCarthy: 8
Tim: 8
Hispanic American: 8
al-Baghdadi: 8
Jim Jordan: 8
Chris: 8
Josh: 8
USMCA: 8
Cuomo: 8
LabCorp: 8
Vladimir Putin: 8
Society: 7
Liberty: 7
Fellow: 7
Resolution: 7
Passamaquoddy Bay: 7
St. Domingo: 7
SENATE: 7
Wabash: 7
HOUSE: 7
Mobile: 7
JAMES MADISON: 7
CONGRESS: 7
Rodgers: 7
Upper: 7
Porter: 7
new States: 7
Seminoles: 7
Apollo: 7
Hamburg: 7
the Corps of Engineers: 7
the State of Illinois: 7
the British Parliament: 7
Southern States: 7
New Brunswick: 7
Federal Union: 7
the French Chambers: 7
Belgians: 7
the Constitution: 7
Ottoman: 7
Sioux: 7
District: 7
Thompson: 7
Harrison: 7
Suffice: 7
the States of Maine: 7
Rouses Point: 7
St. Regis: 7
San Jacinto: 7
Balize: 7
The Mexican Government: 7
Slidell: 7
Van Buren: 7
Galveston: 7
the South American States: 7
the Republic of Venezuela: 7
the State of California: 7
the States of Central America: 7
Sioux Indians: 7
the Government of Nicaragua: 7
St. Lawrence: 7
Greytown: 7
Departments of the Government: 7
Bulwer: 7
the Republic of Honduras: 7
Nelson: 7
The Post-Office Department: 7
Victoria: 7
Miramon: 7
Abraham Baldwin: 7
the South American Republics: 7
Vanderbilt: 7
the Potomac River: 7
Dakota: 7
the City of Washington: 7
Federal and State: 7
Hong Kong: 7
North Germany: 7
the Department: 7
the Commissioners of the District of Columbia: 7
the Government of China: 7
Roumania: 7
Pacific Railway: 7
Austria-Hungary: 7
Miles: 7
Puritan: 7
section 1754: 7
Rule III: 7
section 2: 7
Barrundia: 7
Montt: 7
Morgan: 7
the Choctaws and Chickasaws: 7
Evans: 7
Mora: 7
Englishmen: 7
Red Cross: 7
Reyes: 7
Brig: 7
Jolo: 7
Arlington: 7
Exposition: 7
isthmian: 7
Acknowledge: 7
Beaupre&#39: 7
the New Panama Canal Company: 7
Porto Rican: 7
Root: 7
the South Atlantic: 7
Balkan: 7
the Lincoln Memorial: 7
Tariff Commission: 7
the Continental Congress: 7
Latvia: 7
Estonia: 7
the Federal Farm Board: 7
Central Europe: 7
The Democratic Party: 7
Benjamin Franklin: 7
Munich: 7
Greenland: 7
Wassell: 7
Marshal Stalin: 7
the United Nations Organization: 7
REA: 7
the North Atlantic Treaty: 7
Rayburn: 7
the Korean war: 7
the Soviet Union's: 7
the Interstate Highway System: 7
Western European: 7
FHA: 7
the International Atomic Energy Agency: 7
Atlas: 7
the National Science Foundation: 7
the Atomic Energy Commission: 7
Dirksen: 7
Adenauer: 7
the Eisenhower Administration: 7
McGee: 7
Minuteman: 7
Drummond: 7
Al Smith: 7
Eastern European: 7
the Atlantic Community: 7
Webb: 7
Birmingham: 7
Panamanians: 7
Barry Goldwater: 7
McCormack: 7
Lenin: 7
President: 7
the South Vietnamese Government: 7
the Government of South Vietnam: 7
Ho Chi Minh: 7
Bob: 7
the New York Times: 7
Glassboro: 7
United Nations Security Council: 7
Ehrlichman: 7
the Warsaw Pact: 7
Vladivostok: 7
the European Community: 7
Mayaguez: 7
Barbara Walters: 7
Namibia: 7
John Paul II: 7
STONE: 7
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Jesus: 7
Sandinista: 7
Gramm-Rudman-Hollings: 7
marijuana: 7
NSC: 7
mosques: 7
Dan Quayle: 7
NIH: 7
Pell: 7
Kurds: 7
ADA: 7
Bill Clinton: 7
the United States Armed Forces: 7
the World Trade Center: 7
the National Institutes of Health: 7
Pell Grants: 7
Laura: 7
Chuck Grassley: 7
the Second Amendment: 7
the American Dream: 7
McConnell: 7
Connie: 7
G20: 7
Kelly: 7
Hillary Clinton: 7
Intel: 7
Trayvon Martin: 7
John Lewis: 7
Paris Climate Accord: 7
Steve Scalise: 7
VFW: 7
Schumer: 7
Louie: 7
SBA: 7
Abbott Laboratories: 7
Johnson &: 7
Jim Inhofe: 7
NASDAQ: 7
Ghani: 7
United: 6
Measures: 6
Sardinia: 6
Creek: 6
Natchez: 6
Institution: 6
New Haven: 6
Austin: 6
Gibraltar: 6
Wilkinson: 6
James Madison: 6
the Danish Government: 6
the British Isles: 6
Hull: 6
the La Plata: 6
the Chesapeake Bay: 6
National Government: 6
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Plymouth: 6
the Virgin Islands: 6
the State of Alabama: 6
the Congress of the Confederation: 6
Portsmouth: 6
supreme: 6
Maysville: 6
Northeast: 6
The Treasury Department: 6
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Federal Governments: 6
the European Governments: 6
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the Argentine Confederation: 6
the Federal Executive: 6
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Valentine: 6
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The Spanish Government: 6
Capital: 6
Corpus Christi: 6
Monterey: 6
the Isthmus of Tehuantepec: 6
German Empire: 6
the Panama Railroad: 6
Peruvian: 6
the Colorado River: 6
Henry: 6
Daniel Webster: 6
the Captain-General: 6
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democrats: 6
Adam: 6
Sound: 6
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the Bay Islands: 6
Fort Leavenworth: 6
Dred Scott: 6
the Nicaraguan Government: 6
John Brown: 6
the National Constitution: 6
Oriental: 6
Andrew Johnson: 6
WILLIAM H. SEWARD: 6
the State of North Carolina: 6
IV: 6
Baird: 6
POST-OFFICE DEPARTMENT: 6
Tex.: 6
The Department of State: 6
the Census Bureau: 6
the Land Office: 6
the Advisory Board: 6
the Government of Venezuela: 6
Melbourne: 6
the Hudson River: 6
the District Commissioners: 6
Swiss: 6
the Comptroller of the Currency: 6
Apaches: 6
the Weather Bureau: 6
Jeannette: 6
Siberia: 6
The Chinese Government: 6
the Government of Colombia: 6
the Ecuadorian Government: 6
the Caroline Islands: 6
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Barbados: 6
Guthrie: 6
the International American Conference: 6
the State of Nebraska: 6
Guiana: 6
the Associated Press: 6
The Department of Justice: 6
Yankee: 6
the Eleventh Census: 6
British Guiana: 6
the North Pacific: 6
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Armenians: 6
the committee of safety: 6
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Boxers: 6
Pan-American: 6
Yellowstone: 6
the Isthmian Canal: 6
the General Board: 6
the University of Virginia: 6
The White House: 6
NASHVILLE: 6
HAY: 6
TELEGRAM - TRANSLATION: 6
BOGOTA: 6
Latin: 6
Henry Cabot Lodge: 6
La Boca: 6
Delta: 6
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the Nicaragua Transit Company: 2
the Black Warrior: 2
the State of Kentucky: 2
Giddings: 2
Virginian: 2
the Virginia Legislature: 2
Thenceforward: 2
it:&ldquo;The Missouri Compromise: 2
Compromise: 2
disturb.&rdquo;I: 2
David Wilmot: 2
The &ldquo;Wilmot: 2
the Illinois Legislature: 2
the Resolutions of the Illinois Legislature: 2
WHOLLY: 2
the &ldquo;slave-dealers&rdquo: 2
Our Declaration of Independence: 2
the States of Nebraska: 2
South Carolinian: 2
EQUAL: 2
keepers?I: 2
GREAT: 2
it.&rdquo;It: 2
FRONT: 2
ASPECT: 2
Missourians: 2
WITHOUT: 2
Union?The: 2
COMPROMISE: 2
&amp;c: 2
WOULD: 2
AMERICAN CITIZENS: 2
Pettit: 2
the Declaration of Independence &ldquo;a: 2
forty: 2
Nebraska Senators: 2
Southerners: 2
pieces&rdquo: 2
INTENDED: 2
EFFECT: 2
south east: 2
the &ldquo;Divine: 2
Nebraska &ldquo;perfectly free&rdquo: 2
EXPRESS: 2
WHOLE: 2
H.R.: 2
Charles Allen: 2
Richard Yates: 2
Phillips: 2
Lo: 2
Gentiles: 2
the Pugets Sound Agricultural Company: 2
the States of that Republic: 2
the Transit Company: 2
the States or Territories of the Union: 2
the Baltic Sea: 2
El Dorado: 2
the Mexican States: 2
Lower California: 2
the Hawaiian Kingdom: 2
the house of representatives: 2
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the Territories of Washington: 2
theAmerican: 2
Republic of Honduras: 2
Panama Railroad: 2
the Water Witch: 2
the Paraguayan Government: 2
Geary: 2
the State of Kansas: 2
Van Vliet: 2
the Government Congress: 2
Topeka State: 2
Lane: 2
Robinson: 2
Justice Taney: 2
Fort Bridger: 2
Salt Lake City: 2
Chihuahua: 2
Virgin Bay: 2
the Republic of: 2
Republic of Mexico: 2
the States of the Confederacy: 2
Tien-tsin: 2
Assumption: 2
the Republics of Honduras: 2
Dalles: 2
Pickett: 2
Comonfort: 2
Miramort: 2
Marquez: 2
Tepic: 2
the Governments of Costa Rica: 2
Thomas Mifflin: 2
Hugh Williamson: 2
William Blount: 2
the Territorial Act: 2
Jonathan Dayton: 2
Charles Pinckney: 2
Gouverneur Morris: 2
John Rutledge: 2
Black Republicanism: 2
a Congressional Slave-Code: 2
Slavery: 2
Popular Sovereignty: 2
a Republican party: 2
Orsini: 2
John Browns: 2
the National Territories: 2
these Free States: 2
Disunionists: 2
END: 2
DARE TO DO OUR DUTY AS WE: 2
Covode: 2
Nero: 2
Wildfire: 2
Fernando J. Moreno: 2
Avenue: 2
The Southern States: 2
the States of the North: 2
Fuchau: 2
Tycoon: 2
Charleston Harbor: 2
National Union: 2
the Cumberland Gap: 2
the Secretaries of State: 2
Accomac: 2

General McClellan: 2
the Russian Empire: 2
cuttings: 2
the Congress of the nation: 2
St. Charles: 2
James C. Conkling: 2
the Capital of Illinois: 2
Meade: 2
republican party: 2
New-England: 2
Key-Stone: 2
Jersey: 2
The Sunny South: 2
Antietam, Murfreesboro: 2
Sam: 2
muddy bayou: 2
Britannic Majesty's Government: 2
the Sierra Nevada: 2
the Washington Aqueduct: 2
The Agricultural Department: 2
Jacob Thompson: 2
Beverley Tucker: 2
George N. Sanders: 2
William C. Cleary: 2
W. HUNTER: 2
John Letcher: 2
William Smith: 2
Ninth: 2
Eleventh: 2
Twelfth: 2
Abandoned Lands: 2
Americanpeople: 2
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Whereas: 2
the Senators and Representatives: 2
League Island: 2
Counsel: 2
the State of Colorado: 2
the house of representatives of: 2
supreme authority: 2
Congress of the United States: 2
Judicial: 2
the North German Union: 2
The Republic of Mexico: 2
the Republics of Hayti: 2
Parana: 2
St. Thomas: 2
the King of Denmark: 2
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Edwin M. Stanton: 2
U. S. Grant: 2
U. S.: 2
Stanton&#39;s: 2
Chronicle: 2
Yours: 2
Griffin: 2
Kans.: 2
the "Act: 2
Article III: 2
the Republics of Peru: 2
article 3: 2
the Republic of the United States of Colombia: 2
St. George: 2
the Agricultural Bureau: 2
ARTICLE XV: 2
the Republic of San Domingo: 2
The Government of San Domingo: 2
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Spanish American: 2
Porto: 2
Danish West: 2
Dutch West Indies: 2
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Edward Thornton: 2
Rhine: 2
the Departments of Government: 2
Bureaus: 2
Chester: 2
Laurens: 2
Newberry: 2
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Catacazy: 2
the Giver of All Good: 2
Bancroft: 2
the Haro Channel: 2
Her Majesty's Government: 2
the Republics of Central: 2
Rock Island: 2
INTERIOR DEPARTMENT: 2
The Ninth Census: 2
Chief Executive: 2
Yara: 2
Departments of Government: 2
the Pay Department: 2
William P. Kellogg: 2
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Register: 2
Radical: 2
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Montijo: 2
The Government of Spain: 2
The Court of Commissioners of Alabama Claims: 2
the Subsistence Department: 2
the Sioux Reservation: 2
Yrujo: 2
Fillmore: 2
Pierce: 2
Commissary Generals: 2
James B. Eads: 2
the Centennial Exhibition: 2
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Rutherford B. Hayes: 2
The Government of Italy: 2
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Hot Springs: 2
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VI: 2
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No Department of the Government: 2
St. Pierre: 2
The Government of China: 2
Apache: 2
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unsettle: 2
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were&mdash: 2
The Universal Postal Union: 2
Pa.: 2
Oreg: 2
Sitting Bull: 2
Comptroller: 2
Believing: 2
section 5: 2
Masonic: 2
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theExecutive: 2
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CHESTER A. ARTHUR: 2
JAMES G. BLAINE: 2
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De Long: 2
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Crook: 2
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The Siamese Government: 2
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Thetis: 2
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Liberty Enlightening the World: 2
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Federal or State: 2
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messenger: 2
shall--
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Postal Rule II: 2
Large: 2
Frederick: 2
Saloman: 2
the Board of Ordnance and Fortifications: 2
Apache Indians: 2
Mount Vernon Barracks: 2
Vesuvius: 2
Petrel: 2
American Navy: 2
theStates: 2
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the Susquehanna River: 2
Williamsport: 2
Congress of July 9, 1888: 2
the Government of Nicaragua the Interoceanic Canal Company: 2
the San Juan River: 2
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David S. Terry: 2
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Terry: 2
the bureaus of the Department: 2
Vandalia: 2
Nipsic: 2
Charles Foster: 2
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George Crook: 2
section 14: 2
Lucius Fairchild: 2
Alfred M. Wilson: 2
Warren G. Sayre: 2
Section 12: 2
the Seminole Nation: 2
Merritt: 2
the Department of the: 2
Rudolph Hering: 2
Samuel M. Gray: 2
Frederick P. Stearns: 2
Lyman: 2
the Official Register: 2
section 13: 2
the Sioux Nation: 2
The International Marine Conference: 2
the Kongo Basin: 2
the Republic of Salvador: 2
Ezeta: 2
San Jose de Guatemala: 2
Champerico: 2
The Nicaragua Canal: 2
the Governments of Mexico: 2
Edmund Monson: 2
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John Ericsson: 2
The Customs Administration Board: 2
the "National Guard: 2
Sisseton: 2
Wahpeton: 2
Pottawatomies: 2
Absentee Shawnees: 2
non-Mormon: 2
Wilford Woodruff: 2
Woodruff: 2
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The Oceanic Steamship Company: 2
Auckland: 2
Auckland to San Francisco: 2
The South Atlantic: 2
Arthur: 2
supreme power: 2
Fonseca: 2
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Article VIII: 2
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Blair: 2
The International Boundary Commission: 2
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Title VII: 2
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section 5576: 2
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Proctor: 2
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The Brazil Steamship Company: 2
the Division of the: 2
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the diplomatic corps: 2
Jenkins: 2
Mare Island: 2
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South Atlantic: 2
the Rocky Mountain States: 2
the Tenth Census: 2
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Ira Ayer: 2
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Liverpool 10,941,800 tons: 2
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Fall River: 2
The Society of the Unemployed: 2
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Joseph Nimmo: 2
the Senate Select Committee on Relations with Canada: 2
the Falls of Niagara: 2
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the San Pedro River: 2
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Konia: 2
Asia Minor: 2
the Postmaster-General for eleven mail routes: 2
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The Court of Private Land Claims: 2
the organized civil community: 2
Cheyenne: 2
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Comanches: 2
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Agricultural: 2
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Unless Government: 2
Malietoa Laupepa: 2
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James H. Blount: 2
Blount: 2
the Department of Texas: 2
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Machias: 2
Castine: 2
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Bluefields: 2
Salvadorean: 2
Aana: 2
Tamasese: 2
the Universal Postal Union: 2
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the Census Office: 2
Comptroller of the Currency: 2
A Proclamation
Whereas the island: 2
Tamatav: 2
the Kiel Canal: 2
Mount St. Elias: 2
Yukon: 2
Erzerum: 2
the Life-Saving Service: 2
the Revenue-Cutter Service: 2
the Steamboat-Inspection Service: 2
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Post-Office Department: 2
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the Central Pacific Railroad: 2
The Weather Bureau: 2
provides--: 2
Lynchings: 2
Zanjon: 2
Sagasta: 2
Hawaiians: 2
Tribal: 2
the Marine Hospital Service: 2
The Union Pacific Railway: 2
the United States Court: 2
the Civil Service: 2
the American National Red Cross: 2
Weyler: 2
Blanco: 2
WILLIAM: 2
the Madrid Government: 2
the Naval Militia: 2
Merrill: 2
San Juan de Puerto Rico: 2
Santiago Harbor: 2
Merrimac: 2
Sampson: 2
Clara Barton: 2
the Duke of Almodovar: 2
Ladrones: 2
George Gray: 2
the Atacama Desert: 2
Lattimer: 2
the Universal Exposition: 2
The Nicaragua Canal Commission: 2
John G. Walker: 2
the Paris Exposition: 2
Handy: 2
the Afognak Forest: 2
section 24: 2
The Dawes Commission: 2
the National Congress: 2
the National Banking Act: 2
the Departments of the Government: 2
Pretoria: 2
the General Act: 2
the Governments of Germany: 2
Tutuila: 2
Article VI: 2
Article VII of the Treaty of Peace: 2
Anglo: 2
the International Union of American Republics: 2
the Philadelphia Commercial Museum: 2
The Grand Army: 2
Lake Tahoe: 2
the University of Michigan: 2
the Naval Observatory: 2
Yamen: 2
Liscurn: 2
the united forces: 2
Marroquin: 2
San Clemente: 2
Restraint: 2
Luke I. Wright: 2
Henry C. Ide: 2
Bernard Moses: 2
Mindanao: 2
Census: 2
the Civil Service Board: 2
the Hall of the House of Representatives: 2
Honesty: 2
Pekin: 2
pan-American: 2
McKinley&#39;s: 2
Doubt: 2
the Bureau of Forestry: 2
Hague: 2
Battle: 2
The Hague Court: 2
ARTICLE I.
: 2
ARTICLE II: 2
Article I and: 2
ARTICLE V.
: 2
ARTICLE VI: 2
ARTICLE VII: 2
ARTICLE VIII: 2
ARTICLE IX: 2
ARTICLE X.
: 2
ARTICLE XI: 2
THEODORE ROOSEVELT
DEPARTMENT OF STATE: 2
CORRESPONDENCE BETWEEN THE DEPARTMENT OF STATE: 2
Keep Department: 2
Communicate: 2
Federico Boyd: 2
the Department of Panama: 2
Royal Mail: 2
Bunau Varilla: 2
Boyd: 2
Amador: 2
Bocas del Toro: 2
Senor Philippe: 2
Foreign Relations: 2
Eagle: 2
article 35: 2
EMBASSY OF THE UNITED STATES: 2
Recognition: 2
Andes: 2
Kingston: 2
Republic of Panama: 2
the Stikine River: 2
the Hague: 2
Gifford Pinchot: 2
the Bureau of Forestry of the Department of Agriculture: 2
the War College: 2
the General Board of the Navy: 2
Seward: 2
Everett: 2
the Panama Railway: 2
gram: 2
L'Enfant: 2
bison: 2
Yosemite: 2
Maker: 2
Thanksgiving Day: 2
the Gulf of Alaska: 2
the Yukon River: 2
the Interparliamentary Union: 2
Macedonia: 2
American Republic: 2
the Hospital Corps: 2
Scandinavian: 2
the Copyright Office: 2
the Yosemite National Park: 2
the Grand Canyon: 2
the Canadian Government: 2
the Philippine Government: 2
John F. Stevens: 2
Consulate: 2
Consulates: 2
the Isthmian Commission: 2
Neill: 2
Tuskegee: 2
the Joint Traffic Association: 2
the Middle Ages: 2
Magoon: 2
the Bureau of American Republics: 2
the Third Conference at Rio: 2
every American Republic: 2
the Tribunal of Paris: 2
the Red Cross Conference: 2
the Governments of Russia: 2
The United States Navy: 2
Ancon: 2
Mallet: 2
Ripley: 2
Bierd: 2
the French Company: 2
W. C. Gorgas: 2
Shouts: 2
Garlington: 2
Nettleton: 2
Federal board or commission: 2
the Great Plains: 2
Australasia: 2
Board of Visitors: 2
the interstate commerce law: 2
Appalachians: 2
Eastern Asia: 2
Meyer: 2
Central Asia: 2
PHILIPPINES: 2
RICO: 2
JAPANESE: 2
The American Government: 2
the organized militia: 2
FederalGovernment: 2
Schedule L: 2
Cotton: 2
the Far West: 2
the Appropriations Committee: 2
the Spitzbergen Islands: 2
Spitzbergen: 2
THE NEAR EAST: 2
The Government of Chile: 2
Central American Republics: 2
The International Bureau of American Republics: 2
Executive Order: 2
REVENUES: 2
the Spooner Act: 2
the American Sugar Refining Company: 2
the Hepburn Act: 2
the Court of Commerce: 2
the interstate Commerce Commission: 2
Monopoly: 2
Knight: 2
the Sherman Act: 2
THE FAR EAST: 2
the Hukuang Loan: 2
Pan American: 2
the Civil Service Act: 2
the Bureau of Engraving: 2
Army Engineers: 2
the Army Engineers: 2
the Coast Artillery: 2
NEED: 2
General Staff: 2
the Court of Appeals of the District of Columbia: 2
The American Bar Association: 2
the Circuit Court of Appeals: 2
Treasury Departments: 2
the postal savings bank: 2
the North Pole: 2
RECLAMATION: 2
the Zoological Park: 2
Fort Myer: 2
the Supreme Court of the District: 2
Gillett: 2
Yale University: 2
the Standard Oil Trust: 2
Standard Oil: 2
the American Tobacco Company: 2
the Porto Rican Tobacco Company: 2
VOLUNTARY: 2
the Powder Trust: 2
the Department of justice: 2
George V: 2
compositeur: 2
Infantry: 2
Mexican Government: 2
The Pan American Union: 2
Frederick the Great: 2
the Foreign Affairs Committee: 2
EFFICIENCY: 2
National Reserve Association: 2
Army Service Corps: 2
the Staff Corps: 2
the General Staff Corps: 2
Illinois River: 2
Ill.: 2
the Republics of Central America: 2
Tacna-Arica: 2
Guayaquil: 2
Habana: 2
united effort: 2
the reserve association: 2
the War College Division: 2
beriberi: 2
the Atlantic Fleet: 2
The Interior Department: 2
the Panama-Pacific International Exposition: 2
Valor: 2
Victoriano Huerta: 2
the Iturbide Bridge: 2
boat&#39;s: 2
Huertista: 2
Mayo: 2
Independence Square: 2
the American Declaration of Independence: 2
the European war: 2
Imperial German Government: 2
Sussex: 2
The Imperial German Government: 2
James, Gentlemen: 2
the National Democratic Convention: 2
Boasting: 2
a Tariff Board: 2
a Trade Commission: 2
the Rural Credits Act: 2
Warehouse Act: 2
the Federal Department of Labor: 2
the Progressive Party: 2
The Trade Commission: 2
the Bureau of Foreign and Domestic Commerce: 2
Davis: 2
the Eastern Mediterranean: 2
Housatonic: 2
Fry: 2
Palermo: 2
Reserve Corps: 2
the County Clerk: 2
Northern France: 2
Brest-Litovsk: 2
Central Empires: 2
the War Trade Board: 2
the Food Administration: 2
the National Committee: 2
Middle West: 2
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Stars and Stripes: 2
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the Transportation Act: 2
Envy: 2
this Capital City: 2
The Labor Board: 2
the Permanent Court of International Justice: 2
the Mixed Claims Commission: 2
Post: 2
WELFARE: 2
Our National Government: 2
HIGHWAYS: 2
the Cape Cod Canal: 2
SHIPPING BOARD: 2
Regulars: 2
Virginians: 2
the French War: 2
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American Nation: 2
George III: 2
Arthur Young: 2
Thomas Shepherd: 2
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the Comptroller General: 2
Locarno: 2
League: 2
our Supreme Court: 2
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Chamber of Commerce: 2
Hooker: 2
The Inland Waterways Corporation: 2
Leonard Wood: 2
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National Capital: 2
Patrick Henry: 2
War Finance Corporation: 2
The American Red Cross: 2
INLAND: 2
the American: 2
Fort Sackville: 2
Henry L. Stimson: 2
Samuel Gompers: 2
Program: 2
Paramaribo: 2
the Postal Service: 2
the Governments: 2
Croats and Slovenes: 2
Aristide Briand: 2
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M. Briand: 2
Borah: 2
Swanson: 2
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the Republic of Poland: 2
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AGRICULTURE: 2
the Radio Commission: 2
VETERANS
: 2
the Law Enforcement Commission: 2
The Payne-Aldrich: 2
Underwood: 2
the Japanese Navy: 2
The Reserve System: 2
Nation&#39;s: 2
National, State: 2
Cordell Hull: 2
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the Kellogg-Briand Pact: 2
the National Credit Corporation: 2
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the Democratic House: 2
Power Commission: 2
that&mdash: 2
the Hawley-Smoot: 2
the States of Arizona: 2
The Democratic House of Representatives: 2
George Washington's: 2
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Governorship: 2
Better Homes: 2
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The Surgeon General: 2
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Southeast: 2
Gothic: 2
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Division: 2
Dante: 2
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Preamble: 2
AAA: 2
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Sabbath: 2
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Inauguration: 2
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China Sea: 2
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United Nations': 2
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GERMANY: 2
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The Crimea Conference: 2
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Tragic: 2
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Pauley: 2
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Tarawa: 2
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Civilian: 2
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Federal and Federally: 2
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Savings: 2
Federal Budget: 2
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the Servicemen's Readjustment Act: 2
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ATOMIC ENERGY: 2
a single Department of National Defense: 2
The Greek Government: 2
the Wagner Act: 2
Morse: 2
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Cooperatives: 2
the International Trade Organization: 2
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anti-Catholic: 2
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Forrest Smith: 2
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Exodus: 2
Teapot Dome: 2
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Meet the Press: 2
Price: 2
G.I.: 2
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Hiss: 2
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Early: 2
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Argonne: 2
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The Food and Drug Administration: 2
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Hammarskjold: 2
the United States': 2
Eastern Germany: 2
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Psalmist: 2
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Federal-State: 2
Heads of Government: 2
the North Atlantic Treaty Organization and Western European Union: 2
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Atomic Energy Conference: 2
an International Agency: 2
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Mutual Security Program: 2
our Foreign Service: 2
The Rural Development Program: 2
The Great Plains Program: 2
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National Parks: 2
Parks: 2
Assistance: 2
Federal technical and: 2
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Commissions: 2
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Longshoremen: 2
Harbor Workers': 2
the District of Columbia Unemployment Insurance Law: 2
the National Housing Act: 2
Urban Planning: 2
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Veterans Pensions: 2
Insular Affairs: 2
the Refugee Relief Act: 2
vista: 2
the Republican Party's: 2
the Armed Services: 2
Creator: 2
Fidelity: 2
a Cabinet Committee: 2
Organization for Trade Cooperation: 2
Next: 2
Eastward: 2
the Seat of Government: 2
the Mid East: 2
85th Cong: 2
the 85th: 2
Strategic Air Command: 2
the Korean Armistice: 2
Arias: 2
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Peckham: 1
J.: 1
the Interparliamentary Union for International Arbitration: 1
this Interparliamentary Union: 1
Oriental Empire: 1
Mukden: 1
the Yalu River: 1
the Auditor for the State: 1
the Louisiana Purchase Exposition: 1
Wagon: 1
W. A. Richards: 1
F. H. Newell: 1
Chief Hydrographer: 1
Fort Riley: 1

# Create a DataFrame from the 2D list
# If you have column names, replace ['Column1', 'Column2', 'Column3'] with your column names
enn = pd.DataFrame(ents, columns=['entied', 'countied'])
enn

enn.reset_index(inplace=True)
enn.drop('index', axis=1, inplace=True)
enn.to_csv('entites_common.csv', index=False)

enn = pd.read_csv("entites_common.csv")
enn

# Entity names and their counts (first 50), can be seen in the entites_common.csv file
entities = {
    "the United States": 8279,
    "Congress": 8159,
    "American": 6276,
    "America": 4351,
    "Americans": 2724,
    "State": 2275,
    "States": 1998,
    "Constitution": 1869,
    "Government": 1582,
    "Senate": 1569,
    "Treasury": 1481,
    "Washington": 1156,
    "Europe": 1037,
    "Mexico": 978,
    "China": 841,
    "Navy": 777,
    "Army": 760,
    "Great Britain": 679,
    "Cuba": 618,
    "the Soviet Union": 613,
    "British": 603,
    "Iraq": 590,
    "Spain": 587,
    "Indian": 568,
    "France": 520,
    "the Federal Government": 516,
    "Indians": 504,
    "Texas": 497,
    "Vietnam": 473,
    "House": 472,
    "Russia": 467,
    "Afghanistan": 458,
    "Soviet": 450,
    "Executive": 442,
    "the House of Representatives": 429,
    "Republican": 429,
    "Republicans": 427,
    "the United Nations": 407,
    "United States": 399,
    "Democrats": 395,
    "Department": 387,
    "Japan": 387,
    "Germany": 386,
    "The United States": 382,
    "Asia": 380,
    "the United States of America": 367,
    "European": 364,
    "the Middle East": 363,
    "Chinese": 361,
    "Pacific": 357,
    "Communist": 354
}

# Create lists for names and counts
names = list(entities.keys())
counts = list(entities.values())

# Create the histogram
plt.figure(figsize=(10, 8))
plt.barh(names, counts, color='skyblue')
plt.xlabel('Counts')
plt.ylabel('Entities')
plt.title('Histogram of Entity Mentions')
plt.gca().invert_yaxis()  # Invert y-axis to have the highest count at the top
plt.show()

'''
woah = pd.read_csv('entities_spec.csv')
listofem=[]

i=0
for ent in enn['entied']:
    print(""+str(i)+'/'+str(len(enn))+"")
    i=i+1
    temp=woah.loc[woah['entity2']== ent]
    now = pd.DataFrame(columns=['year', ent])
    for date in dates:
        if date in temp['year'].values:
            ddd=temp.loc[temp["year"]== date]
            ddd.reset_index(inplace=True)
            ddd.drop('index', axis=1, inplace=True)
            now.loc[len(now)] = [ date,  int(ddd.loc[0,"county"])]
        else:
            now.loc[len(now)] = [ date,  0]
    listofem.append(now)
    
letsgo = pd.DataFrame()
letsgo

for i in range(len(listofem)):
    print(i)
    letsgo= pd.concat([letsgo, listofem[i][listofem[i].columns[1]] ], axis=1)

letsgo.reset_index(inplace=True)
letsgo.drop('index', axis=1, inplace=True)
letsgo.to_csv('entities_counted.csv', index=False)

'''

'\nwoah = pd.read_csv(\'entities_spec.csv\')\nlistofem=[]\n\ni=0\nfor ent in enn[\'entied\']:\n    print(""+str(i)+\'/\'+str(len(enn))+"")\n    i=i+1\n    temp=woah.loc[woah[\'entity2\']== ent]\n    now = pd.DataFrame(columns=[\'year\', ent])\n    for date in dates:\n        if date in temp[\'year\'].values:\n            ddd=temp.loc[temp["year"]== date]\n            ddd.reset_index(inplace=True)\n            ddd.drop(\'index\', axis=1, inplace=True)\n            now.loc[len(now)] = [ date,  int(ddd.loc[0,"county"])]\n        else:\n            now.loc[len(now)] = [ date,  0]\n    listofem.append(now)\n    \nletsgo = pd.DataFrame()\nletsgo\n\nfor i in range(len(listofem)):\n    print(i)\n    letsgo= pd.concat([letsgo, listofem[i][listofem[i].columns[1]] ], axis=1)\n\nletsgo.reset_index(inplace=True)\nletsgo.drop(\'index\', axis=1, inplace=True)\nletsgo.to_csv(\'entities_counted.csv\', index=False)\n\n'

letsgo = pd.read_csv("entities_counted.csv")
letsgo

# Re-creating the DataFrame with U.S. Presidents and their party affiliations
presidents_df = pd.DataFrame({
    "President": ["George Washington", "John Adams", "Thomas Jefferson", "James Madison", 
                  "James Monroe", "John Quincy Adams", "Andrew Jackson", "Martin Van Buren", 
                  "William Henry Harrison", "John Tyler", "James K. Polk", "Zachary Taylor", 
                  "Millard Fillmore", "Franklin Pierce", "James Buchanan", "Abraham Lincoln", 
                  "Andrew Johnson", "Ulysses S. Grant", "Rutherford B. Hayes", "James A. Garfield", 
                  "Chester A. Arthur", "Grover Cleveland", "Benjamin Harrison", "Grover Cleveland", 
                  "William McKinley", "Theodore Roosevelt", "William Howard Taft", "Woodrow Wilson", 
                  "Warren G. Harding", "Calvin Coolidge", "Herbert Hoover", "Franklin D. Roosevelt", 
                  "Harry S. Truman", "Dwight D. Eisenhower", "John F. Kennedy", "Lyndon B. Johnson", 
                  "Richard Nixon", "Gerald Ford", "Jimmy Carter", "Ronald Reagan", 
                  "George H. W. Bush", "Bill Clinton", "George W. Bush", "Barack Obama", 
                  "Donald Trump", "Joe Biden"],
    "Start Year": [1789, 1797, 1801, 1809, 1817, 1825, 1829, 1837, 1841, 1841, 1845, 1849, 
                   1850, 1853, 1857, 1861, 1865, 1869, 1877, 1881, 1881, 1885, 1889, 1893, 
                   1897, 1901, 1909, 1913, 1921, 1923, 1929, 1933, 1945, 1953, 1961, 1963, 
                   1969, 1974, 1977, 1981, 1989, 1993, 2001, 2009, 2017, 2021],
    "End Year": [1797, 1801, 1809, 1817, 1825, 1829, 1837, 1841, 1841, 1845, 1849, 1850, 
                 1853, 1857, 1861, 1865, 1869, 1877, 1881, 1881, 1885, 1889, 1893, 1897, 
                 1901, 1909, 1913, 1921, 1923, 1929, 1933, 1945, 1953, 1961, 1963, 1969, 
                 1974, 1977, 1981, 1989, 1993, 2001, 2009, 2017, 2021, 2025], # Assuming Biden's term ends in 2025
    "Party": ["None (Independent)", "Federalist", "Democratic-Republican", "Democratic-Republican", 
              "Democratic-Republican", "Democratic-Republican", "Democrat", "Democrat", 
              "Whig", "Whig/None (Independent)", "Democrat", "Whig", "Whig", "Democrat", 
              "Democrat", "Republican", "Democratic/National Union", "Republican", "Republican", 
              "Republican", "Republican", "Democrat", "Republican", "Democrat", "Republican", 
              "Republican", "Republican", "Democrat", "Republican", "Republican", "Republican", 
              "Democrat", "Democrat", "Republican", "Democrat", "Democrat", "Republican", 
              "Republican", "Democrat", "Republican", "Republican", "Democrat", "Republican", 
              "Democrat", "Republican", "Democrat"]
})

# Now creating the year-by-year DataFrame
yearly_data = []

# Iterate over each year from 1789 to 2023
for year in range(1789, 2024):
    # Find the president in office for that year
    president_in_office = presidents_df[(presidents_df['Start Year'] <= year) & (presidents_df['End Year'] > year)]
    if not president_in_office.empty:
        president_name = president_in_office.iloc[0]['President']
        party = president_in_office.iloc[0]['Party']
    else:
        # Continuing from where the previous code left off to complete the DataFrame creation
        president_name = None
        party = None
    yearly_data.append({"Year": year, "President": president_name, "Party": party})

# Convert the list to a DataFrame
yearly_presidents_df = pd.DataFrame(yearly_data)

# Display the first few rows to check the result
yearly_presidents_df.head()

letsgo

# Applying the function to each year
letsgo.insert(0, '_Party_', yearly_presidents_df['Party'])
letsgo

letsgo.insert(0, '_Year_', dates)

letsgo

letsgo.to_csv('entities_counted_Partied.csv', index=False)
letsgo

letsgo = pd.read_csv("entities_counted_Partied.csv")
letsgo

# Assuming df is your DataFrame

# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
letgos = letsgo.drop(columns=letsgo.columns[0])
letgos = letgos.drop(columns=letgos.columns[0])

# Scale the data
letgos_scaled = pd.DataFrame(scaler.fit_transform(letgos), columns=letgos.columns, index=letsgo["_Year_"])

# Create the heatmap
plt.figure(figsize=(50, 100))
sns.heatmap(letgos_scaled.T, annot=False, cmap='Blues')
plt.show()

data = [
    "Europe: 1037", "Germany: 386", "Hitler: 56", "Jews: 34", 
    "World War II: 157", "Ukraine: 137", "Turkey: 134"
]

# Splitting each string at ':' and taking the first part
cleaned_list_Europe = [(item.split(':')[0] )for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Europe

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20)
plt.ylabel('Sum', fontsize=20)
plt.title('World War Entites Sum Non Democrat' , fontsize=20)
plt.xticks(rotation=45, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)

# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Republicn President Speeches since US presidency specific to World Wars.", wrap=True, horizontalalignment='center', fontsize=13)

plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=45, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('World War Entites Sum Non Republican')

# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to World Wars.", wrap=True, horizontalalignment='center', fontsize=13)

plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_Europe]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Democrat, including whips and non denomination parties, President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_Europe]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)


# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)


#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_Europe]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)


# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2528904079.py:67: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2528904079.py:101: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2528904079.py:139: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "Cuba: 618", "Panama: 261", "Mexico: 978", "Nicaragua: 224",
    "Cuban: 174", "the Panama Canal: 73", "Porto Rico: 72"
]

# Splitting each string at ':' and taking the first part
cleaned_list_South_American = [(item.split(':')[0] )for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_South_American

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entities', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('South American Entites Sum Non Democrat', fontsize =20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to South American Involvement.", wrap=True, horizontalalignment='center', fontsize=13)

plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entities', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation= 75, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('South American Entites Sum Non Republican', fontsize = 20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to South American Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_South_American]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_South_American]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_South_American]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3754587022.py:68: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3754587022.py:102: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3754587022.py:137: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "the Soviet Union: 613", "Russia: 467", "Soviet: 450",
    "Communist: 354", "Soviets: 177", "Communists: 175"
]


# Splitting each string at ':' and taking the first part
# Splitting each string at ':' and taking the first part
cleaned_list_russian = [item.split(':')[0] for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_russian

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Russian Entites Sum Non Democrat', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.55, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Russian Involvements.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Russian Entites Sum Non Republican', fontsize= 20)

# Add a caption
plt.figtext(0.5, -0.55, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Russian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_russian]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Cold War Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_russian]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Cold War Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_russian]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Russian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Russian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Russian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)


#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/170823032.py:71: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/170823032.py:105: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/170823032.py:141: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "Iraq: 590", "the Middle East: 363", "Afghanistan: 458", "Iran: 282", 
    "Iraqi: 191", "Taliban: 118", "Afghan: 112", "al Qaeda: 106", 
    "Arab: 99", "Saddam Hussein: 75", "Saddam Hussein's: 13", 
    "Iraqis: 75", "Somalia: 73", "Muslim: 48", "Al Qaeda: 45", 
    "Osama bin Laden: 30", "Afghans: 30", "Assad: 30"
]



# Splitting each string at ':' and taking the first part
# Splitting each string at ':' and taking the first part
cleaned_list_Middle_east = [item.split(':')[0] for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Middle_east

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Middle East Entites Sum Non Democrat', fontsize= 20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Middle Eastern Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Middle East Entites Sum Non Republican', fontsize= 20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Middle Eastern Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_Middle_east]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Middle Eastern Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_Middle_east]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Middle Eastern Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_Middle_east]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Middle East Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25,10))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Middle East Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Middle East Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3291119174.py:75: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3291119174.py:109: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3291119174.py:145: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "Vietnam: 473", "Japan: 387", "Viet-Nam: 145", "South Vietnam: 165",
    "Korea: 174", "Philippines: 160", "Japanese: 157",
    "South Vietnamese: 112", "South Viet-Nam: 111", "North Vietnam: 111",
    "China: 841", "Chinese: 361"
]



# Splitting each string at ':' and taking the first part
# Splitting each string at ':' and taking the first part
cleaned_list_Asia = [item.split(':')[0] for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Asia

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Asian Entites Sum Non Democrat', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Asian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)

plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Asian Entites Sum Non Republican', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Asian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_Asia]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_Asia]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_Asia]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824117806.py:73: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824117806.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824117806.py:143: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "Israel: 332", "Palestinians: 70", "Palestinian: 68", 
    "Israelis: 62", "Palestine: 26", "Israeli: 58", "Jews: 34"
]


# Splitting each string at ':' and taking the first part
cleaned_list_I_P = [item.split(':')[0] for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_I_P

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=45, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('I/P Entites Sum Non Democrat', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Isreal and Palestinian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=45, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('I/P Entites Sum Non Republican', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Israel and Palestinian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)

plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_I_P]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_I_P]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_I_P]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824223548.py:69: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824223548.py:103: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824223548.py:139: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "Negro: 84", "the Civil War: 56", "African American: 34", 
    "Negroes: 25", "Africans: 22", "African-American: 15", 
    "African-Americans: 15", "Native American: 15"
]


# Splitting each string at ':' and taking the first part
cleaned_list_Minority = [item.split(':')[0] for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Minority

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Minority Entites Sum Non Democrat', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Minority Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Minority Entites Sum Non Republican', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Minority Involvement.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_Minority]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_Minority]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_Minority]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 7))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1375633772.py:71: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1375633772.py:105: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1375633772.py:141: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

data = [
    "the United Nations: 407", "Medicare: 252", "Ford: 190", "Kennedy: 276",
    "Trump: 91", "Truman: 74", "Lincoln: 73", "Martin Luther King: 31",
    "Bush: 163", "Reagan: 149", "Nixon: 147", "Pakistan: 81",
    "Watergate: 73", "the Civil War: 56", "COVID-19: 62", "Great Britain: 679",
    "British: 603"
]



# Splitting each string at ':' and taking the first part
cleaned_list_assorted = [item.split(':')[0] for item in data]


##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']

import matplotlib.pyplot as plt

# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_assorted

# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_democrat_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20, labelpad=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Other Entites Sum Non Democrat', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.65, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency non-specific to any event or topic but that seemed of interest.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()


# Calculate the sum of each column
sums = []
for column in subs:
    sums.append(non_Republican_df[column].sum())

# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20, labelpad=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15)  # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Other Entites Sum Non Republican', fontsize=20)

# Add a caption
plt.figtext(0.5, -0.65, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency non-specific to any event or topic but that seemed of interest.", wrap=True, horizontalalignment='center', fontsize=13)


plt.show()




def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

subs=non_democrat_df[cleaned_list_assorted]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years Non Democrat\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()






subs=non_Republican_df[cleaned_list_assorted]

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()


result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)

# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years Non Republican\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

##
##
##


subs=letsgo[cleaned_list_assorted]
def map_to_interval(year):
    return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"

# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

# Group by interval and sum
result_df = subs.groupby('Interval').sum()

result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)



# Set the size of the heatmap
plt.figure(figsize=(25, 10))  # You can adjust the size as needed

# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples')  # 'annot=False' hides the data values, 'cmap' sets the color map

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

# Assuming df is your DataFrame



# Initialize a Min-Max Scaler
scaler = MinMaxScaler()

# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years MinMax Scaled\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()



# Initialize a Standard Scaler
scaler = StandardScaler()

# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)

# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')

plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years Standardized\n', fontsize=25)

# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)

#plt.savefig('Speech_heatmap.png');
plt.show()

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2766536643.py:74: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2766536643.py:108: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2766536643.py:144: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)

file=pd.read_csv("outputfinal_filename.csv")
file

## i made a seperate python file that can be run in the folder woth this file
## Refer to the python file 'neurl_coref.py'

'''
!pip install spacey==2.1.1
!pip install neuralcoref

import spacy
import neuralcoref
import pandas as pd

file=pd.read_csv("outputfinal_filename.csv")

nlp = spacy.load('en_core_web_sm')
neuralcoref.add_to_pipe(nlp)

overall = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])

for i in range(len(file)):
    prez=str(file.loc[i, "president"])
    trans=str(file.loc[i, "transcript"])
    prez= prez.replace(" ", "")
    final= trans.replace('I ',""+prez+" " )
    doc=nlp(final)
    resolved_text = doc._.coref_resolved
    
    overall.loc[i, 'doc_name']= file.loc[i, "doc_name"]
    overall.loc[i, 'date']= file.loc[i, "date"]
    overall.loc[i, 'transcript']= resolved_text
    overall.loc[i, 'president']= file.loc[i, "president"]
    overall.loc[i, 'title']= file.loc[i, "title"]
    overall.loc[i, 'Year']= file.loc[i, "Year"]
    
    print(""+str(i)+"/"+str(len(file))+"")
    
    
    
    
overall.reset_index(inplace=True)
overall.drop('index', axis=1, inplace=True)   
overall.to_csv('neuraled.csv', index=False)

'''

'\n!pip install spacey==2.1.1\n!pip install neuralcoref\n\nimport spacy\nimport neuralcoref\nimport pandas as pd\n\nfile=pd.read_csv("outputfinal_filename.csv")\n\nnlp = spacy.load(\'en_core_web_sm\')\nneuralcoref.add_to_pipe(nlp)\n\noverall = pd.DataFrame(columns=[\'doc_name\', \'date\', \'transcript\', \'president\', \'title\', \'Year\'])\n\nfor i in range(len(file)):\n    prez=str(file.loc[i, "president"])\n    trans=str(file.loc[i, "transcript"])\n    prez= prez.replace(" ", "")\n    final= trans.replace(\'I \',""+prez+" " )\n    doc=nlp(final)\n    resolved_text = doc._.coref_resolved\n    \n    overall.loc[i, \'doc_name\']= file.loc[i, "doc_name"]\n    overall.loc[i, \'date\']= file.loc[i, "date"]\n    overall.loc[i, \'transcript\']= resolved_text\n    overall.loc[i, \'president\']= file.loc[i, "president"]\n    overall.loc[i, \'title\']= file.loc[i, "title"]\n    overall.loc[i, \'Year\']= file.loc[i, "Year"]\n    \n    print(""+str(i)+"/"+str(len(file))+"")\n    \n    \n    \n    \noverall.reset_index(inplace=True)\noverall.drop(\'index\', axis=1, inplace=True)   \noverall.to_csv(\'neuraled.csv\', index=False)\n\n'

file=pd.read_csv("neuraled.csv")
file

# refer to the python file "relation.py"
'''
file=pd.read_csv("neuraled.csv")
# Load spaCy model
nlp = spacy.load('en_core_web_sm')
relations = pd.DataFrame(columns=['entity1','entity2',  'sentiment',  'date',  'president', 'title', 'Year'])

for e in range(int(len(file))):
    
    text = file.loc[e, 'transcript' ]
    print(""+str(e)+"/"+str(int(len(file)))+"")

    # Process the text
    doc = nlp(text)

    # Assuming we are interested in relations involving two proper nouns (entities)
    entities = [ent for ent in doc if ent.pos_ == 'PROPN']

    
    ## precations to make sure two word entites are not being analysied seperately
    p=0
    # Extracting relationships and performing sentiment analysis
    for i in range(len(entities)-1):
        entity1 = entities[i]

        # Example: Checking if entities appear in the same sentence
        combined_entities = str(entity1) + ' ' + str(entities[i+1])
                                                     
        if p>0:
            p=p-1
            continue
        k=0
        if combined_entities in entity1.sent.text:
            entity1= combined_entities
            k=k+1
        for j in range(i+1, len(entities)):
            entity2 = entities[j]

            if k==1:
                k=k-1
                p=p+1
                continue

            if (j < len(entities)-2):
                # Example: Checking if entities appear in the same sentence
                combined_entities = str(entity2) + ' ' + str(entities[j+1])

            if combined_entities in entity2.sent.text:
                entity2= combined_entities





            # Example: Checking if entities appear in the same sentence

            if entities[i].sent == entities[j].sent :
                # Sentiment analysis on the sentence
                sentiment = TextBlob(str(entities[i].sent)).sentiment
                
                
                rel = pd.DataFrame({
                    'entity1': [entity1],
                    'entity2': [entity2],

                    'sentiment': [sentiment.polarity], # Polarity score: -1 (negative) to 1 (positive)
     
                    'date': [file.loc[e, 'date']],
    
                    'president': [file.loc[e, 'president']],
                    'title': [file.loc[e, 'title']],
                    'Year': [file.loc[e, 'Year']]
                })
                relations=pd.concat([relations,rel],ignore_index=True) 
                
relations.reset_index(inplace=True)
relations.drop('index', axis=1, inplace=True)   
relations.to_csv('relationships1.csv', index=False) 

'''

'\nfile=pd.read_csv("neuraled.csv")\n# Load spaCy model\nnlp = spacy.load(\'en_core_web_sm\')\nrelations = pd.DataFrame(columns=[\'entity1\',\'entity2\',  \'sentiment\',  \'date\',  \'president\', \'title\', \'Year\'])\n\nfor e in range(int(len(file))):\n    \n    text = file.loc[e, \'transcript\' ]\n    print(""+str(e)+"/"+str(int(len(file)))+"")\n\n    # Process the text\n    doc = nlp(text)\n\n    # Assuming we are interested in relations involving two proper nouns (entities)\n    entities = [ent for ent in doc if ent.pos_ == \'PROPN\']\n\n    \n    ## precations to make sure two word entites are not being analysied seperately\n    p=0\n    # Extracting relationships and performing sentiment analysis\n    for i in range(len(entities)-1):\n        entity1 = entities[i]\n\n        # Example: Checking if entities appear in the same sentence\n        combined_entities = str(entity1) + \' \' + str(entities[i+1])\n                                                     \n        if p>0:\n            p=p-1\n            continue\n        k=0\n        if combined_entities in entity1.sent.text:\n            entity1= combined_entities\n            k=k+1\n        for j in range(i+1, len(entities)):\n            entity2 = entities[j]\n\n            if k==1:\n                k=k-1\n                p=p+1\n                continue\n\n            if (j < len(entities)-2):\n                # Example: Checking if entities appear in the same sentence\n                combined_entities = str(entity2) + \' \' + str(entities[j+1])\n\n            if combined_entities in entity2.sent.text:\n                entity2= combined_entities\n\n\n\n\n\n            # Example: Checking if entities appear in the same sentence\n\n            if entities[i].sent == entities[j].sent :\n                # Sentiment analysis on the sentence\n                sentiment = TextBlob(str(entities[i].sent)).sentiment\n                \n                \n                rel = pd.DataFrame({\n                    \'entity1\': [entity1],\n                    \'entity2\': [entity2],\n\n                    \'sentiment\': [sentiment.polarity], # Polarity score: -1 (negative) to 1 (positive)\n     \n                    \'date\': [file.loc[e, \'date\']],\n    \n                    \'president\': [file.loc[e, \'president\']],\n                    \'title\': [file.loc[e, \'title\']],\n                    \'Year\': [file.loc[e, \'Year\']]\n                })\n                relations=pd.concat([relations,rel],ignore_index=True) \n                \nrelations.reset_index(inplace=True)\nrelations.drop(\'index\', axis=1, inplace=True)   \nrelations.to_csv(\'relationships1.csv\', index=False) \n\n'

# Remove setiments that equal 0
relate= pd.read_csv('relationships1.csv')
relate=relate.loc[relate["sentiment"]!= 0.0]
relate.reset_index(inplace=True)
relate.drop('index', axis=1, inplace=True)   
relate.to_csv('relationships1.csv', index=False)

relate= pd.read_csv('relationships1.csv')
relate

chart = pd.DataFrame(columns=['entity1','entity2',  'avg_sentiment', 'entity_count', 'connect_count'])

'''
dup = relate.drop_duplicates(subset='entity1')
dup.reset_index(inplace=True)
dup.drop('index', axis=1, inplace=True)

for i in range(len(dup)):
    print(str(i)+'/'+str(len(dup)))
    st=dup.loc[i, "entity1"]
    temp1=relate.loc[relate['entity1']== st]
    
    num1=len(temp1)
    
    
    
    dum = temp1.drop_duplicates(subset='entity2')
    dum.reset_index(inplace=True)
    dum.drop('index', axis=1, inplace=True)
    
    for j in range(len(dum)):
        sm=dum.loc[j, "entity2"]
        temp2=temp1.loc[temp1['entity2']== sm]
        
        avg_sent=(temp2['sentiment'].sum())/len(temp2)
        num2=len(temp2)
        
        rel = pd.DataFrame({
                    'entity1': [st],
                    'entity2': [sm],
                    'avg_sentiment': [avg_sent], # Polarity score: -1 (negative) to 1 (positive)
                    'entity_count' : [num1],
                    'connect_count': [num2]
                })
        chart=pd.concat([chart,rel],ignore_index=True)
chart.to_csv('chart.csv', index=False)  

'''

'\ndup = relate.drop_duplicates(subset=\'entity1\')\ndup.reset_index(inplace=True)\ndup.drop(\'index\', axis=1, inplace=True)\n\nfor i in range(len(dup)):\n    print(str(i)+\'/\'+str(len(dup)))\n    st=dup.loc[i, "entity1"]\n    temp1=relate.loc[relate[\'entity1\']== st]\n    \n    num1=len(temp1)\n    \n    \n    \n    dum = temp1.drop_duplicates(subset=\'entity2\')\n    dum.reset_index(inplace=True)\n    dum.drop(\'index\', axis=1, inplace=True)\n    \n    for j in range(len(dum)):\n        sm=dum.loc[j, "entity2"]\n        temp2=temp1.loc[temp1[\'entity2\']== sm]\n        \n        avg_sent=(temp2[\'sentiment\'].sum())/len(temp2)\n        num2=len(temp2)\n        \n        rel = pd.DataFrame({\n                    \'entity1\': [st],\n                    \'entity2\': [sm],\n                    \'avg_sentiment\': [avg_sent], # Polarity score: -1 (negative) to 1 (positive)\n                    \'entity_count\' : [num1],\n                    \'connect_count\': [num2]\n                })\n        chart=pd.concat([chart,rel],ignore_index=True)\nchart.to_csv(\'chart.csv\', index=False)  \n\n'

chart

chart= pd.read_csv('chart.csv')
chart

chart_ordered=chart.sort_values(by='entity_count', ascending=False)
chart_ordered=chart_ordered.sort_values(["entity_count", "connect_count"],ascending=False )

chart_ordered.reset_index(inplace=True)
chart_ordered.drop('index', axis=1, inplace=True)
chart_ordered = chart_ordered[~chart_ordered.apply(lambda row: row['entity2'] in row['entity1'], axis=1)]
chart_ordered.reset_index(inplace=True)
chart_ordered.drop('index', axis=1, inplace=True)
chart_ordered.to_csv('chart_ordered.csv', index=False)
chart_ordered

charted= pd.read_csv('chart_ordered.csv')
charted

months=['January', 'February', 'March', 'April', 'May', 'June', 
 'July', 'August', 'September', 'October', 'November', 'December']

count=0
for i in range(len(charted)):
    jack=str(charted.loc[i,'entity2'])
    for month in months:
        if month in jack :
            charted = charted.drop(i)
            #count+=1

charted

charted.reset_index(inplace=True)
charted.drop('index', axis=1, inplace=True)
charted

charted.loc[charted['avg_sentiment']<-0.9]

# reove counts and connections less than 30, reomove entite\y2 which are substrings of entity1
chartoy=charted.loc[charted['entity_count']>29]
chartoy=chartoy.loc[charted['connect_count']>29]
chartoy.reset_index(inplace=True)
chartoy.drop('index', axis=1, inplace=True)
chartoy = chartoy[~chartoy.apply(lambda row: row['entity2'] in row['entity1'], axis=1)]
chartoy.reset_index(inplace=True)
chartoy.drop('index', axis=1, inplace=True)
chartoy.to_csv('chart_ordy.csv', index=False)
chartoy

chartoy.loc[chartoy['avg_sentiment']<0]

chartoy = chartoy.drop_duplicates(subset='entity1')
chartoy.reset_index(inplace=True)
chartoy.drop('index', axis=1, inplace=True)
chartoy

/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3296870759.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  chartoy.drop('index', axis=1, inplace=True)

original_rows_to_remove = [249, 245, 236, 232, 221, 188, 177, 160, 129, 128, 118, 115, 110, 105, 90, 84, 76, 75, 69, 56, 54, 52, 48, 39, 38, 36, 31, 24]

# Adjusting to 0-based indexing by subtracting 1 from each index
rows_to_remove = [x - 1 for x in original_rows_to_remove]

# Remove the specified rows
chartoy = chartoy.drop(rows_to_remove)

# Optional: Reset index if you want a continuous index after removal
chartoy = chartoy.reset_index(drop=True)

chartoy

def ploting_map(data_new, stwing= "Knowledge Map of Entity Relationships"):
    # Creating a new graph
    G_new = nx.Graph()

    # Extracting all unique entities
    all_entities_new = set(data_new['entity1']).union(set(data_new['entity2']))

    # Creating a dictionary for entity sizes
    default_size = 100  # Default size
    entity_sizes_new = {entity: data_new[data_new['entity1'] == entity]['entity_count'].iloc[0]
                        if entity in data_new['entity1'].values else default_size 
                        for entity in all_entities_new}

    # Adding nodes with their sizes, scaled 5 times bigger
    for entity, size in entity_sizes_new.items():
        G_new.add_node(entity, size=size * 50)  # Scaling the node size by 5 times

    # Adding edges with varying thickness and color based on sentiment
    for _, row in data_new.iterrows():
        width = abs(row['avg_sentiment']) * 10  # Scale the width
        color = 'red' if row['avg_sentiment'] < 0 else 'blue'
        G_new.add_edge(row['entity1'], row['entity2'], weight=width, color=color)

    # Extracting sizes for nodes, scaled 5 times bigger
    sizes_new_scaled = [G_new.nodes[node]['size']/200 for node in G_new.nodes]

    # Extracting colors and weights for edges
    edge_colors_new = nx.get_edge_attributes(G_new,'color').values()
    edge_weights_new = nx.get_edge_attributes(G_new,'weight').values()

    pos = nx.spring_layout(G_new, k=1.5, iterations=20) 

    # Drawing the graph with scaled node sizes
    plt.figure(figsize=(10, 8))
    nx.draw(G_new,pos, with_labels=True, node_size=sizes_new_scaled, width=list(edge_weights_new), edge_color=list(edge_colors_new), node_color='skyblue', edgecolors='black')
    plt.title(stwing)
    plt.show()

ploting_map(charted.loc[charted['avg_sentiment']<0][:100], "100-150 negative sentiment over .75")

ploting_map(charted.loc[charted['avg_sentiment']<0][:400], "First 400 negative Sent. Relation Knowledge Map")

ploting_map(chartoy.loc[chartoy['avg_sentiment']<0], "all negative Sent. with counts more than 30")

ploting_map(chartoy.loc[chartoy['avg_sentiment']>0.25], "top positive Sent. of 0ver 0.25 Relation Knowledge Map top relation for each entity")

	doc_name	date	transcript	president	title	Year
1049	march-26-2022-remarks-support-people-ukraine	2022-03-26	THE PRESIDENT: Thank you, thank you, thank you...	Joe Biden	March 26, 2022: Remarks in Support of the Peop...	2022
1050	may-24-2022-remarks-school-shooting-uvalde-texas	2022-05-25	Good evening, fellow Americans.\r\n\r\nI had h...	Joe Biden	May 24, 2022: Remarks on School Shooting in Uv...	2022
1051	september-1-2022-remarks-continued-battle-soul...	2022-09-02	THE PRESIDENT: My fellow Americans, please, if...	Joe Biden	September 1, 2022: Remarks on the Continued Ba...	2022
1052	september-21-2022-speech-77th-session-united-n...	2022-09-21	Thank you. \r\n\r\nMr. President, Mr. Secretar...	Joe Biden	September 21, 2022: Speech before the 77th Ses...	2022
1053	february-7-2023-state-union-address	2023-02-08	Mr. Speaker. Madam Vice President. Our First L...	Joe Biden	February 7, 2023: State of the Union Address	2023
1054	february-21-2023-remarks-one-year-anniversary-...	2023-02-22	THE PRESIDENT: Hello, Poland! One of our grea...	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023
1055	NaN	January 21, 1946	\nTo the Congress of the United States:\nA qua...	Harry S. Truman	STATE OF THE UNION January 21, 1946	Janu
1056	NaN	January 4, 1950	\nMr. President, Mr. Speaker, Members of the C...	Harry S. Truman	STATE OF THE UNION January 4, 1950	Janu
1057	NaN	January 5, 1949	\nMr. President, Mr. Speaker, Members of the C...	Harry S. Truman	STATE OF THE UNION January 5, 1949	Janu
1058	NaN	January 6, 1947	\nMr. President, Mr. Speaker, Members of the C...	Harry S. Truman	STATE OF THE UNION January 6, 1947	Janu
1059	NaN	January 7, 1948	\nMr. President, Mr. Speaker, and Members of t...	Harry S. Truman	STATE OF THE UNION January 7, 1948	Janu
1060	NaN	January 8, 1951	\nMr. President, Mr. Speaker, Members Of the C...	Harry S. Truman	STATE OF THE UNION January 8, 1951	Janu

	doc_name	date	transcript	president	title	Year
0	april-30-1789-first-inaugural-address	1789-04-30	Fellow Citizens of the Senate and the House of...	George Washington	April 30, 1789: First Inaugural Address	1789
1	october-3-1789-thanksgiving-proclamation	1789-10-03	Whereas it is the duty of all Nations to ackno...	George Washington	October 3, 1789: Thanksgiving Proclamation	1789
2	january-8-1790-first-annual-message-congress	1790-01-08	Fellow Citizens of the Senate and House of Rep...	George Washington	January 8, 1790: First Annual Message to Congress	1790
3	december-8-1790-second-annual-message-congress	1790-12-08	Fellow citizens of the Senate and House of Re...	George Washington	December 8, 1790: Second Annual Message to Con...	1790
4	december-29-1790-talk-chiefs-and-counselors-se...	1790-12-29	I the President of the United States, by my o...	George Washington	December 29, 1790: Talk to the Chiefs and Coun...	1790
...	...	...	...	...	...	...
1056	may-24-2022-remarks-school-shooting-uvalde-texas	2022-05-25	Good evening, fellow Americans.\r\n\r\nI had h...	Joe Biden	May 24, 2022: Remarks on School Shooting in Uv...	2022
1057	september-1-2022-remarks-continued-battle-soul...	2022-09-02	THE PRESIDENT: My fellow Americans, please, if...	Joe Biden	September 1, 2022: Remarks on the Continued Ba...	2022
1058	september-21-2022-speech-77th-session-united-n...	2022-09-21	Thank you. \r\n\r\nMr. President, Mr. Secretar...	Joe Biden	September 21, 2022: Speech before the 77th Ses...	2022
1059	february-7-2023-state-union-address	2023-02-08	Mr. Speaker. Madam Vice President. Our First L...	Joe Biden	February 7, 2023: State of the Union Address	2023
1060	february-21-2023-remarks-one-year-anniversary-...	2023-02-22	THE PRESIDENT: Hello, Poland! One of our grea...	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023

	doc_name	date	transcript	president	title	Year
0	april-30-1789-first-inaugural-address	1789-04-30	Fellow Citizens of the Senate and the House of...	George Washington	April 30, 1789: First Inaugural Address	1789
1	october-3-1789-thanksgiving-proclamation	1789-10-03	Whereas it is the duty of all Nations to ackno...	George Washington	October 3, 1789: Thanksgiving Proclamation	1789
2	january-8-1790-first-annual-message-congress	1790-01-08	Fellow Citizens of the Senate and House of Rep...	George Washington	January 8, 1790: First Annual Message to Congress	1790
3	december-8-1790-second-annual-message-congress	1790-12-08	Fellow citizens of the Senate and House of Re...	George Washington	December 8, 1790: Second Annual Message to Con...	1790
4	december-29-1790-talk-chiefs-and-counselors-se...	1790-12-29	I the President of the United States, by my o...	George Washington	December 29, 1790: Talk to the Chiefs and Coun...	1790
...	...	...	...	...	...	...
1056	may-24-2022-remarks-school-shooting-uvalde-texas	2022-05-25	Good evening, fellow Americans.\r\n\r\nI had h...	Joe Biden	May 24, 2022: Remarks on School Shooting in Uv...	2022
1057	september-1-2022-remarks-continued-battle-soul...	2022-09-02	THE PRESIDENT: My fellow Americans, please, if...	Joe Biden	September 1, 2022: Remarks on the Continued Ba...	2022
1058	september-21-2022-speech-77th-session-united-n...	2022-09-21	Thank you. \r\n\r\nMr. President, Mr. Secretar...	Joe Biden	September 21, 2022: Speech before the 77th Ses...	2022
1059	february-7-2023-state-union-address	2023-02-08	Mr. Speaker. Madam Vice President. Our First L...	Joe Biden	February 7, 2023: State of the Union Address	2023
1060	february-21-2023-remarks-one-year-anniversary-...	2023-02-22	THE PRESIDENT: Hello, Poland! One of our grea...	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023

	doc_name	date	transcript	president	title
0	april-18-1977-address-nation-energy	1977-04-18	Good evening.\r\nTonight I want to have an unp...	Jimmy Carter	April 18, 1977: Address to the Nation on Energy
1	april-25-1980-statement-iran-rescue-mission	1980-04-25	Late yesterday, I cancelled a carefully planne...	Jimmy Carter	April 25, 1980: Statement on the Iran Rescue M...
2	august-14-1980-acceptance-speech-democratic-na...	1980-08-14	Fellow Democrats, fellow citizens:\r\n\r\nI th...	Jimmy Carter	August 14, 1980: Acceptance Speech at the Demo...

	doc_name	date	transcript	president	title	Year
0	april-30-1789-first-inaugural-address	1789-04-30	Fellow Citizens of the Senate and the House of...	George Washington	April 30, 1789: First Inaugural Address	1789
1	october-3-1789-thanksgiving-proclamation	1789-10-03	Whereas it is the duty of all Nations to ackno...	George Washington	October 3, 1789: Thanksgiving Proclamation	1789
2	january-8-1790-first-annual-message-congress	1790-01-08	Fellow Citizens of the Senate and House of Rep...	George Washington	January 8, 1790: First Annual Message to Congress	1790
3	december-8-1790-second-annual-message-congress	1790-12-08	Fellow citizens of the Senate and House of Re...	George Washington	December 8, 1790: Second Annual Message to Con...	1790
4	december-29-1790-talk-chiefs-and-counselors-se...	1790-12-29	I the President of the United States, by my o...	George Washington	December 29, 1790: Talk to the Chiefs and Coun...	1790
...	...	...	...	...	...	...
1056	may-24-2022-remarks-school-shooting-uvalde-texas	2022-05-25	Good evening, fellow Americans.\r\n\r\nI had h...	Joe Biden	May 24, 2022: Remarks on School Shooting in Uv...	2022
1057	september-1-2022-remarks-continued-battle-soul...	2022-09-02	THE PRESIDENT: My fellow Americans, please, if...	Joe Biden	September 1, 2022: Remarks on the Continued Ba...	2022
1058	september-21-2022-speech-77th-session-united-n...	2022-09-21	Thank you. \r\n\r\nMr. President, Mr. Secretar...	Joe Biden	September 21, 2022: Speech before the 77th Ses...	2022
1059	february-7-2023-state-union-address	2023-02-08	Mr. Speaker. Madam Vice President. Our First L...	Joe Biden	February 7, 2023: State of the Union Address	2023
1060	february-21-2023-remarks-one-year-anniversary-...	2023-02-22	THE PRESIDENT: Hello, Poland! One of our grea...	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023

Through out this code i comment some code out cause it takes to long to run, so if you wanted to run this code, I have data already stored to a csv so the code that takes long to be run doesnt need to be run.¶

extracting data from JSON file¶

Adding in speeches not included like speeches done by Eisenhower and Truman¶

SO the data collection is done now, no doing some data cleaning¶

Not part of My code from Spring, NICOLE SEMERANO'S code¶

NICOLE'S CODE STOPS HERE¶

SO the data collection is done now, now doing NLP PROCESSING¶

Named Entity Recognition¶

Data processing¶

Case Study 1¶

Case Study 2¶

This took really long to run, but it basically looks through all the speeches and extracts the ENtites mentioned and saves them in a list¶

Saving the data for later use¶

Now counting the number of each entity have been detected¶

Bar Chart of count of first 50 most common Entites¶

These took really long so i saved the data so you dont have to run it¶

Chart of alot of Entites, not final product, just checking (charts take time to be made)¶

Chart 1¶

chart 2¶

Chart 3¶

Chart 4¶

Chart 5¶

Chart 6¶

Chart 7¶

Chart 8¶

Case Studies¶

Relation Extraction¶

This took really long and coldnt be run in Juypter, cause it didnt let me install Neuralcoref due to Juypter inability to install spacey 2.1.1, so made an environment through the terminal and was abel to install Neuracoref in conda¶

This took really long¶

This took really long¶

Messing Around with Data starting here to figure out how to represent the knowledge maps¶

Plotting Knowdlege map of the¶

	doc_name	date	transcript	president	title	Year
1055	NaN	1946-01-21	\nTo the Congress of the United States:\nA qua...	Harry S. Truman	STATE OF THE UNION January 21, 1946	Janu
1056	NaN	1950-01-04	\nMr. President, Mr. Speaker, Members of the C...	Harry S. Truman	STATE OF THE UNION January 4, 1950	Janu
1057	NaN	1949-01-05	\nMr. President, Mr. Speaker, Members of the C...	Harry S. Truman	STATE OF THE UNION January 5, 1949	Janu
1058	NaN	1947-01-06	\nMr. President, Mr. Speaker, Members of the C...	Harry S. Truman	STATE OF THE UNION January 6, 1947	Janu
1059	NaN	1948-01-07	\nMr. President, Mr. Speaker, and Members of t...	Harry S. Truman	STATE OF THE UNION January 7, 1948	Janu
1060	NaN	1951-01-08	\nMr. President, Mr. Speaker, Members Of the C...	Harry S. Truman	STATE OF THE UNION January 8, 1951	Janu

	year	entity	entity2	county
0	1789	Fellow Citizens: 1	Fellow Citizens	1
1	1789	Senate: 1	Senate	1
2	1789	the House of Representatives: 2	the House of Representatives	2
3	1789	Country: 2	Country	2
4	1789	Act: 1	Act	1
...	...	...	...	...
44855	2023	First Lady: 1	First Lady	1
44856	2023	Russians: 1	Russians	1
44857	2023	Allies: 2	Allies	2
44858	2023	NATO Alliance: 1	NATO Alliance	1
44859	2023	Article 5: 1	Article 5	1

	entied	countied
0	the United States	8285
1	Congress	8163
2	American	6278
3	America	4351
4	Americans	2724
...	...	...
16093	Moldovan	1
16094	Sandu	1
16095	Kherson	1
16096	the Orange Revolution	1
16097	the Heavenly Hundred	1

	the United States	Congress	American	America	Americans	State	States	Constitution	Government	Senate	...	this House of Representatives	Paul Pelosi	Hello	the NATOization of Finland	the Warsaw Uprising	Moldovan	Sandu	Kherson	the Orange Revolution	the Heavenly Hundred
0	3	0	2	0	0	0	0	1	0	1	...	0	0	0	0	0	0	0	0	0	0
1	22	3	0	0	0	1	1	1	0	3	...	0	0	0	0	0	0	0	0	0	0
2	14	1	0	0	0	0	1	1	2	2	...	0	0	0	0	0	0	0	0	0	0
3	4	4	0	0	0	0	3	4	1	2	...	0	0	0	0	0	0	0	0	0	0
4	18	6	0	0	0	0	2	1	0	2	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
230	53	26	54	47	28	0	1	1	0	4	...	0	0	0	0	0	0	0	0	0	0
231	80	47	212	138	123	0	1	19	0	20	...	0	0	0	0	0	0	0	0	0	0
232	54	25	213	177	136	3	0	6	0	23	...	0	0	0	0	0	0	0	0	0	0
233	46	12	60	91	45	1	0	11	0	4	...	0	0	0	0	0	0	0	0	0	0
234	8	11	26	39	19	0	0	0	0	2	...	1	1	1	1	1	1	1	1	1	1

	Year	President	Party
0	1789	George Washington	None (Independent)
1	1790	George Washington	None (Independent)
2	1791	George Washington	None (Independent)
3	1792	George Washington	None (Independent)
4	1793	George Washington	None (Independent)

	the United States	Congress	American	America	Americans	State	States	Constitution	Government	Senate	...	this House of Representatives	Paul Pelosi	Hello	the NATOization of Finland	the Warsaw Uprising	Moldovan	Sandu	Kherson	the Orange Revolution	the Heavenly Hundred
0	3	0	2	0	0	0	0	1	0	1	...	0	0	0	0	0	0	0	0	0	0
1	22	3	0	0	0	1	1	1	0	3	...	0	0	0	0	0	0	0	0	0	0
2	14	1	0	0	0	0	1	1	2	2	...	0	0	0	0	0	0	0	0	0	0
3	4	4	0	0	0	0	3	4	1	2	...	0	0	0	0	0	0	0	0	0	0
4	18	6	0	0	0	0	2	1	0	2	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
230	53	26	54	47	28	0	1	1	0	4	...	0	0	0	0	0	0	0	0	0	0
231	80	47	212	138	123	0	1	19	0	20	...	0	0	0	0	0	0	0	0	0	0
232	54	25	213	177	136	3	0	6	0	23	...	0	0	0	0	0	0	0	0	0	0
233	46	12	60	91	45	1	0	11	0	4	...	0	0	0	0	0	0	0	0	0	0
234	8	11	26	39	19	0	0	0	0	2	...	1	1	1	1	1	1	1	1	1	1

	entity1	entity2	sentiment	date	president	title	Year
0	GeorgeWashington	Country	0.233333	1789-04-30	George Washington	April 30, 1789: First Inaugural Address	1789
1	GeorgeWashington	GeorgeWashington	0.233333	1789-04-30	George Washington	April 30, 1789: First Inaugural Address	1789
2	GeorgeWashington	GeorgeWashington	0.233333	1789-04-30	George Washington	April 30, 1789: First Inaugural Address	1789
3	Country	GeorgeWashington	0.233333	1789-04-30	George Washington	April 30, 1789: First Inaugural Address	1789
4	Country	GeorgeWashington	0.233333	1789-04-30	George Washington	April 30, 1789: First Inaugural Address	1789
...	...	...	...	...	...	...	...
497537	Ukraine	— Ukraine	0.044583	2023-02-22	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023
497538	Ukraine	Ukraine	0.044583	2023-02-22	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023
497539	Ukraine —	Ukraine	0.044583	2023-02-22	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023
497540	United States	Allies	0.027778	2023-02-22	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023
497541	’s	Americans	0.350000	2023-02-22	Joe Biden	February 21, 2023: Remarks on the One-Year Ann...	2023

	entity1	entity2	avg_sentiment	entity_count	connect_count
0	GeorgeWashington	Country	0.079034	570	10
1	GeorgeWashington	GeorgeWashington	0.219698	570	141
2	GeorgeWashington	Act	0.123333	570	3
3	GeorgeWashington	Almighty	0.398333	570	3
4	GeorgeWashington	Universe	0.185667	570	5
...	...	...	...	...	...
147543	Hundred	’s	-0.500000	1	1
147544	Madam First	Lady	0.375000	4	1
147545	Madam First	United States	0.375000	4	1
147546	Madam First	States	0.375000	4	1
147547	Madam First	Europe	0.375000	4	1

	entity1	entity2	avg_sentiment	entity_count	connect_count
0	United States	Government	0.093470	40135	1574
1	United States	America	0.115637	40135	969
2	United States	President	0.084311	40135	901
3	United States	Congress	0.102111	40135	714
4	United States	Union	0.088627	40135	467
...	...	...	...	...	...
141863	Applied Physics	Lab	0.200000	1	1
141864	List	COVID-19	0.107143	1	1
141865	thatHe	’s	0.800000	1	1
141866	militaryHe	’s	-0.100000	1	1
141867	Q:—to Rick	Bright	0.700000	1	1

	entity1	entity2	avg_sentiment	entity_count	connect_count
635	United States	Ukraine —	-1.0	40135	4
636	United States	— Ukraine	-1.0	40135	4
13160	Union	Korea	-1.0	3623	1
13161	Union	U.S.	-1.0	3623	1
13162	Union	North Korea	-1.0	3623	1
...	...	...	...	...	...
136833	Paul II	God	-1.0	2	1
137057	Nineteen	Nancy	-1.0	2	1
137058	Nineteen	RonaldReagan	-1.0	2	1
137504	Life Synagogue	Pittsburgh	-1.0	1	1
138202	Beast	Belsen	-1.0	1	1

	entity1	entity2	avg_sentiment	entity_count	connect_count
50	United States	Island	-0.016570	40135	66
72	United States	Rhode Island	-0.044448	40135	53
96	United States	Departments	-0.010184	40135	39
100	United States	Minister	-0.031579	40135	38
104	United States	Affairs	-0.034864	40135	35
122	United States	Foreign Affairs	-0.046774	40135	31
272	President	Island	-0.032758	14738	51
275	President	Rhode Island	-0.045748	14738	49
278	President	Executive	-0.028899	14738	46
331	State	Affairs	-0.036293	8538	35
332	State	France	-0.025873	8538	35
341	State	Minister	-0.051667	8538	30
342	State	Foreign Affairs	-0.051667	8538	30
365	Secretary	France	-0.035937	7244	32
366	Secretary	Foreign Affairs	-0.050968	7244	31
369	Secretary	Minister	-0.017742	7244	31
475	House	Island	-0.050035	3840	49
476	House	Rhode Island	-0.058333	3840	48
480	House	Executive	-0.047724	3840	34
513	Treasury	Bank	-0.036632	3369	35
525	Republic	Cuba	-0.035573	3356	47
718	Executive	Island	-0.013556	1527	55
719	Executive	Rhode Island	-0.017356	1527	54
937	JamesBuchanan	President	-0.026490	885	34
1061	Rhode Island	Executive	-0.049221	581	53
1062	Rhode Island	States	-0.047956	581	35
1084	Nam	America	-0.018788	550	33
1085	Nam	Russia	-0.006875	550	32
1086	Nam	India	-0.006875	550	32
1087	Nam	China	-0.006875	550	32
1163	Minister	France	-0.037857	290	34
1164	Minister	Affairs	-0.028800	290	33
1165	Minister	Foreign Affairs	-0.044330	290	32
1181	Ukraine	Warsaw	-0.262366	249	31
1189	Foreign Affairs	France	-0.037857	236	34
1232	Osama bin	Laden	-0.043686	120	40
1242	t Senator	Kennedy	-0.038372	91	44

	the United States	Congress	American	America	Americans	State	States	Constitution	Government	Senate	...	this House of Representatives	Paul Pelosi	Hello	the NATOization of Finland	the Warsaw Uprising	Moldovan	Sandu	Kherson	the Orange Revolution	the Heavenly Hundred
0	3	0	2	0	0	0	0	1	0	1	...	0	0	0	0	0	0	0	0	0	0
1	22	3	0	0	0	1	1	1	0	3	...	0	0	0	0	0	0	0	0	0	0
2	14	1	0	0	0	0	1	1	2	2	...	0	0	0	0	0	0	0	0	0	0
3	4	4	0	0	0	0	3	4	1	2	...	0	0	0	0	0	0	0	0	0	0
4	18	6	0	0	0	0	2	1	0	2	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
230	53	26	54	47	28	0	1	1	0	4	...	0	0	0	0	0	0	0	0	0	0
231	80	47	212	138	123	0	1	19	0	20	...	0	0	0	0	0	0	0	0	0	0
232	54	25	213	177	136	3	0	6	0	23	...	0	0	0	0	0	0	0	0	0	0
233	46	12	60	91	45	1	0	11	0	4	...	0	0	0	0	0	0	0	0	0	0
234	8	11	26	39	19	0	0	0	0	2	...	1	1	1	1	1	1	1	1	1	1

	the United States	Congress	American	America	Americans	State	States	Constitution	Government	Senate	...	this House of Representatives	Paul Pelosi	Hello	the NATOization of Finland	the Warsaw Uprising	Moldovan	Sandu	Kherson	the Orange Revolution	the Heavenly Hundred
0	3	0	2	0	0	0	0	1	0	1	...	0	0	0	0	0	0	0	0	0	0
1	22	3	0	0	0	1	1	1	0	3	...	0	0	0	0	0	0	0	0	0	0
2	14	1	0	0	0	0	1	1	2	2	...	0	0	0	0	0	0	0	0	0	0
3	4	4	0	0	0	0	3	4	1	2	...	0	0	0	0	0	0	0	0	0	0
4	18	6	0	0	0	0	2	1	0	2	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
230	53	26	54	47	28	0	1	1	0	4	...	0	0	0	0	0	0	0	0	0	0
231	80	47	212	138	123	0	1	19	0	20	...	0	0	0	0	0	0	0	0	0	0
232	54	25	213	177	136	3	0	6	0	23	...	0	0	0	0	0	0	0	0	0	0
233	46	12	60	91	45	1	0	11	0	4	...	0	0	0	0	0	0	0	0	0	0
234	8	11	26	39	19	0	0	0	0	2	...	1	1	1	1	1	1	1	1	1	1

	the United States	Congress	American	America	Americans	State	States	Constitution	Government	Senate	...	this House of Representatives	Paul Pelosi	Hello	the NATOization of Finland	the Warsaw Uprising	Moldovan	Sandu	Kherson	the Orange Revolution	the Heavenly Hundred
0	3	0	2	0	0	0	0	1	0	1	...	0	0	0	0	0	0	0	0	0	0
1	22	3	0	0	0	1	1	1	0	3	...	0	0	0	0	0	0	0	0	0	0
2	14	1	0	0	0	0	1	1	2	2	...	0	0	0	0	0	0	0	0	0	0
3	4	4	0	0	0	0	3	4	1	2	...	0	0	0	0	0	0	0	0	0	0
4	18	6	0	0	0	0	2	1	0	2	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
230	53	26	54	47	28	0	1	1	0	4	...	0	0	0	0	0	0	0	0	0	0
231	80	47	212	138	123	0	1	19	0	20	...	0	0	0	0	0	0	0	0	0	0
232	54	25	213	177	136	3	0	6	0	23	...	0	0	0	0	0	0	0	0	0	0
233	46	12	60	91	45	1	0	11	0	4	...	0	0	0	0	0	0	0	0	0	0
234	8	11	26	39	19	0	0	0	0	2	...	1	1	1	1	1	1	1	1	1	1

	the United States	Congress	American	America	Americans	State	States	Constitution	Government	Senate	...	this House of Representatives	Paul Pelosi	Hello	the NATOization of Finland	the Warsaw Uprising	Moldovan	Sandu	Kherson	the Orange Revolution	the Heavenly Hundred
0	3	0	2	0	0	0	0	1	0	1	...	0	0	0	0	0	0	0	0	0	0
1	22	3	0	0	0	1	1	1	0	3	...	0	0	0	0	0	0	0	0	0	0
2	14	1	0	0	0	0	1	1	2	2	...	0	0	0	0	0	0	0	0	0	0
3	4	4	0	0	0	0	3	4	1	2	...	0	0	0	0	0	0	0	0	0	0
4	18	6	0	0	0	0	2	1	0	2	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
230	53	26	54	47	28	0	1	1	0	4	...	0	0	0	0	0	0	0	0	0	0
231	80	47	212	138	123	0	1	19	0	20	...	0	0	0	0	0	0	0	0	0	0
232	54	25	213	177	136	3	0	6	0	23	...	0	0	0	0	0	0	0	0	0	0
233	46	12	60	91	45	1	0	11	0	4	...	0	0	0	0	0	0	0	0	0	0
234	8	11	26	39	19	0	0	0	0	2	...	1	1	1	1	1	1	1	1	1	1